llama : more tokenizer fixes (#2810 )

* tests : write a Python tokenizer test (wip) * llama : prefix input text for tokenization with whitespace * llama : distinguish pieces from decoded text + fix detokenization * common : add comments * examples : no longer manually add leading space when tokenizing * tests : use Python to generate tokenizer tests for C++ * tests : add option to tokenize text files ggml-ci * tests : add test-tokenizer-1.py * llama.cpp : fix LF token * hellaswag : move the concat space for clarity * tests : add falcon tests (py + cpp, currently do not pass Unicode) ggml-ci * common : temporary separate llama_detokenize calls for SPM and BPE --------- Co-authored-by: klosax <131523366+klosax@users.noreply.github.com>
ggml : detect SSSE3 (#2825 )
2026-05-07 09:34:07 +00:00 · 2023-08-27 14:19:19 +03:00 · 2023-08-27 11:10:25 +03:00 · 2023-08-27 10:03:27 +03:00 · 2023-08-27 07:11:45 +08:00 · 2023-08-26 23:13:36 +03:00
27 changed files with 1069 additions and 399 deletions
--- a/ci/run.sh
+++ b/ci/run.sh
@@ -196,17 +196,17 @@ function gg_run_open_llama_3b_v2 {
    (time ./bin/main --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
    (time ./bin/main --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log

-    (time ./bin/perplexity --model ${model_f16}  -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+    (time ./bin/perplexity --model ${model_f16}  -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log

    function check_ppl {
        qnt="$1"
@@ -233,6 +233,48 @@ function gg_run_open_llama_3b_v2 {
    check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
    check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log

+    # lora
+    function compare_ppl {
+        qnt="$1"
+        ppl1=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
+        ppl2=$(echo "$3" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
+
+        if [ $(echo "$ppl1 < $ppl2" | bc) -eq 1 ]; then
+            printf '  - %s @ %s (FAIL: %s > %s)\n' "$qnt" "$ppl" "$ppl1" "$ppl2"
+            return 20
+        fi
+
+        printf '  - %s @ %s %s OK\n' "$qnt" "$ppl1" "$ppl2"
+        return 0
+    }
+
+    path_lora="../models-mnt/open-llama/3B-v2/lora"
+    path_shakespeare="../models-mnt/shakespeare"
+
+    shakespeare="${path_shakespeare}/shakespeare.txt"
+    lora_shakespeare="${path_lora}/ggml-adapter-model.bin"
+
+    gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/adapter_config.json
+    gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/adapter_model.bin
+    gg_wget ${path_shakespeare} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/shakespeare.txt
+
+    python3 ../convert-lora-to-ggml.py ${path_lora}
+
+    # f16
+    (time ./bin/perplexity --model ${model_f16} -f ${shakespeare}                            -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log
+    (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log
+    compare_ppl "f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-f16.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
+
+    # q8_0
+    (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare}                            -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log
+    (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log
+    compare_ppl "q8_0 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
+
+    # q8_0 + f16 lora-base
+    (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log
+    compare_ppl "q8_0 / f16 base shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
+
+
    set +e
 }

@@ -242,6 +284,7 @@ function gg_sum_open_llama_3b_v2 {
    gg_printf 'OpenLLaMA 3B-v2:\n'
    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
    gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
+    gg_printf '- lora:\n%s\n' "$(cat $OUT/${ci}-lora-ppl.log)"
    gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
    gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
    gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
@@ -253,6 +296,11 @@ function gg_sum_open_llama_3b_v2 {
    gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
    gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
    gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
+    gg_printf '- shakespeare (f16):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-f16.log)"
+    gg_printf '- shakespeare (f16 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)"
+    gg_printf '- shakespeare (q8_0):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)"
+    gg_printf '- shakespeare (q8_0 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log)"
+    gg_printf '- shakespeare (q8_0 / f16 base lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log)"
 }

 # open_llama_7b_v2
@@ -310,17 +358,17 @@ function gg_run_open_llama_7b_v2 {
    ./bin/quantize ${model_f16} ${model_q5_k} q5_k
    ./bin/quantize ${model_f16} ${model_q6_k} q6_k

-    (time ./bin/main --model ${model_f16}  -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/main --model ${model_q8_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/main --model ${model_q4_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/main --model ${model_q4_1} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/main --model ${model_q5_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/main --model ${model_q5_1} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/main --model ${model_q2_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/main --model ${model_q3_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/main --model ${model_q4_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/main --model ${model_q5_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/main --model ${model_q6_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+    (time ./bin/main --model ${model_f16}  -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/main --model ${model_q8_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/main --model ${model_q4_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/main --model ${model_q4_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/main --model ${model_q5_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/main --model ${model_q5_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/main --model ${model_q2_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/main --model ${model_q3_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/main --model ${model_q4_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/main --model ${model_q5_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/main --model ${model_q6_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log

    (time ./bin/perplexity --model ${model_f16}  -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
    (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
@@ -359,6 +407,48 @@ function gg_run_open_llama_7b_v2 {
    check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
    check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log

+    # lora
+    function compare_ppl {
+        qnt="$1"
+        ppl1=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
+        ppl2=$(echo "$3" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
+
+        if [ $(echo "$ppl1 < $ppl2" | bc) -eq 1 ]; then
+            printf '  - %s @ %s (FAIL: %s > %s)\n' "$qnt" "$ppl" "$ppl1" "$ppl2"
+            return 20
+        fi
+
+        printf '  - %s @ %s %s OK\n' "$qnt" "$ppl1" "$ppl2"
+        return 0
+    }
+
+    path_lora="../models-mnt/open-llama/7B-v2/lora"
+    path_shakespeare="../models-mnt/shakespeare"
+
+    shakespeare="${path_shakespeare}/shakespeare.txt"
+    lora_shakespeare="${path_lora}/ggml-adapter-model.bin"
+
+    gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/adapter_config.json
+    gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/adapter_model.bin
+    gg_wget ${path_shakespeare} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/shakespeare.txt
+
+    python3 ../convert-lora-to-ggml.py ${path_lora}
+
+    # f16
+    (time ./bin/perplexity --model ${model_f16} -f ${shakespeare}                            -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log
+    (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log
+    compare_ppl "f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-f16.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
+
+    # currently not supported by the CUDA backend
+    # q8_0
+    #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare}                            -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log
+    #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log
+    #compare_ppl "q8_0 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
+
+    # q8_0 + f16 lora-base
+    #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log
+    #compare_ppl "q8_0 / f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
+
    set +e
 }

@@ -368,6 +458,7 @@ function gg_sum_open_llama_7b_v2 {
    gg_printf 'OpenLLaMA 7B-v2:\n'
    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
    gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
+    gg_printf '- lora:\n%s\n' "$(cat $OUT/${ci}-lora-ppl.log)"
    gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
    gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
    gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
@@ -379,6 +470,11 @@ function gg_sum_open_llama_7b_v2 {
    gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
    gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
    gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
+    gg_printf '- shakespeare (f16):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-f16.log)"
+    gg_printf '- shakespeare (f16 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)"
+    #gg_printf '- shakespeare (q8_0):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)"
+    #gg_printf '- shakespeare (q8_0 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log)"
+    #gg_printf '- shakespeare (q8_0 / f16 base lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log)"
 }

 ## main
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -733,12 +733,12 @@ std::vector<llama_token> llama_tokenize(
    return result;
 }

-std::string llama_token_to_str(const struct llama_context * ctx, llama_token token) {
+std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
    std::vector<char> result(8, 0);
-    const int n_tokens = llama_token_to_str(ctx, token, result.data(), result.size());
+    const int n_tokens = llama_token_to_piece(ctx, token, result.data(), result.size());
    if (n_tokens < 0) {
        result.resize(-n_tokens);
-        int check = llama_token_to_str(ctx, token, result.data(), result.size());
+        int check = llama_token_to_piece(ctx, token, result.data(), result.size());
        GGML_ASSERT(check == -n_tokens);
    } else {
        result.resize(n_tokens);
@@ -746,3 +746,36 @@ std::string llama_token_to_str(const struct llama_context * ctx, llama_token tok

    return std::string(result.data(), result.size());
 }
+
+std::string llama_detokenize_spm(llama_context * ctx, const std::vector<llama_token> & tokens) {
+    const llama_token bos_id = llama_token_bos(ctx);
+
+    std::string piece;
+    std::string result;
+
+    for (size_t i = 0; i < tokens.size(); ++i) {
+        piece = llama_token_to_piece(ctx, tokens[i]);
+
+        // remove the leading space of the first non-BOS token
+        if (((tokens[0] == bos_id && i == 1) || (tokens[0] != bos_id && i == 0)) && piece[0] == ' ') {
+            piece = piece.substr(1);
+        }
+
+        result += piece;
+    }
+
+    return result;
+}
+
+std::string llama_detokenize_bpe(llama_context * ctx, const std::vector<llama_token> & tokens) {
+    std::string piece;
+    std::string result;
+
+    for (size_t i = 0; i < tokens.size(); ++i) {
+        piece = llama_token_to_piece(ctx, tokens[i]);
+
+        result += piece;
+    }
+
+    return result;
+}
--- a/common/common.h
+++ b/common/common.h
@@ -116,11 +116,31 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
 // Vocab utils
 //

+// tokenizes a string into a vector of tokens
+// should work similar to Python's `tokenizer.encode`
 std::vector<llama_token> llama_tokenize(
        struct llama_context * ctx,
           const std::string & text,
                        bool   add_bos);

-std::string llama_token_to_str(
+// tokenizes a token into a piece
+// should work similar to Python's `tokenizer.id_to_piece`
+std::string llama_token_to_piece(
        const struct llama_context * ctx,
                       llama_token   token);
+
+// TODO: these should be moved in llama.h C-style API under single `llama_detokenize` function
+//       that takes into account the tokenizer type and decides how to handle the leading space
+//
+// detokenizes a vector of tokens into a string
+// should work similar to Python's `tokenizer.decode`
+// removes the leading space from the first non-BOS token
+std::string llama_detokenize_spm(
+                         llama_context * ctx,
+        const std::vector<llama_token> & tokens);
+
+// detokenizes a vector of tokens into a string
+// should work similar to Python's `tokenizer.decode`
+std::string llama_detokenize_bpe(
+                         llama_context * ctx,
+        const std::vector<llama_token> & tokens);
--- a/convert.py
+++ b/convert.py
@@ -3,6 +3,7 @@
 import gguf
 import argparse
 import concurrent.futures
+from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
 import copy
 import enum
 import faulthandler
@@ -17,13 +18,14 @@ import re
 import signal
 import struct
 import sys
+import time
 import zipfile
 import numpy as np

 from abc import ABCMeta, abstractmethod
 from dataclasses import dataclass
 from pathlib import Path
-from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Literal, Optional, Sequence, Tuple, TypeVar, Union)
+from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Generator, Iterable, List, Literal, Optional, Sequence, Set, Tuple, TypeVar, Union)
 from sentencepiece import SentencePieceProcessor  # type: ignore

 if TYPE_CHECKING:
@@ -37,30 +39,70 @@ NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
 ARCH=gguf.MODEL_ARCH.LLAMA
 NAMES=gguf.MODEL_TENSOR_NAMES[ARCH]

+DEFAULT_CONCURRENCY = 8
 #
 # data types
 #

@dataclass(frozen=True)
-class UnquantizedDataType:
+class DataType:
    name: str
+    dtype: 'np.dtype[Any]'
+    valid_conversions: List[str]

-DT_F16  = UnquantizedDataType('F16')
-DT_F32  = UnquantizedDataType('F32')
-DT_I32  = UnquantizedDataType('I32')
-DT_BF16 = UnquantizedDataType('BF16')
+    def elements_to_bytes(self, n_elements: int) -> int:
+        return n_elements * self.dtype.itemsize

-DataType = Union[UnquantizedDataType]
+@dataclass(frozen=True)
+class UnquantizedDataType(DataType):
+    pass

-DATA_TYPE_TO_NUMPY: Dict[DataType, 'np.dtype[Any]'] = {
-    DT_BF16: np.dtype(np.uint16),
-    DT_F16:  np.dtype(np.float16),
-    DT_F32:  np.dtype(np.float32),
-    DT_I32:  np.dtype(np.int32),
-}
+DT_F16  = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0'])
+DT_F32  = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0'])
+DT_I32  = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = [])
+DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0'])

-NUMPY_TYPE_TO_DATA_TYPE: Dict['np.dtype[Any]', DataType] = \
-    {dtype: data_type for (data_type, dtype) in DATA_TYPE_TO_NUMPY.items()}
+@dataclass(frozen=True)
+class QuantizedDataType(DataType):
+    block_size: int
+    quantized_dtype: 'np.dtype[Any]'
+    ggml_type: gguf.GGMLQuantizationType
+
+    def quantize(self, arr: NDArray) -> NDArray:
+        raise NotImplementedError(f'Quantization for {self.name} not implemented')
+
+    def elements_to_bytes(self, n_elements: int) -> int:
+        assert n_elements % self.block_size == 0, f'Invalid number of elements {n_elements} for {self.name} with block size {self.block_size}'
+        return self.quantized_dtype.itemsize * (n_elements // self.block_size)
+
+@dataclass(frozen=True)
+class Q8_0QuantizedDataType(QuantizedDataType):
+    # Mini Q8_0 quantization in Python!
+    def quantize(self, arr: NDArray) -> NDArray:
+        assert arr.size % self.block_size == 0 and arr.size != 0, f'Bad array size {arr.size}'
+        assert arr.dtype == np.float32, f'Bad array type {arr.dtype}'
+        n_blocks = arr.size // self.block_size
+        blocks = arr.reshape((n_blocks, self.block_size))
+        # Much faster implementation of block quantization contributed by @Cebtenzzre
+        def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[Tuple[Any, Any]]:
+            d = abs(blocks).max(axis = 1) / np.float32(127)
+            with np.errstate(divide = 'ignore'):
+                qs = (blocks / d[:, None]).round()
+            qs[d == 0] = 0
+            yield from zip(d, qs)
+        return np.fromiter(quantize_blocks_q8_0(blocks), count = n_blocks, dtype = self.quantized_dtype)
+
+DT_Q8_0 = Q8_0QuantizedDataType('Q8_0',
+    dtype = np.dtype(np.float32), valid_conversions = [],
+    ggml_type = gguf.GGMLQuantizationType.Q8_0, block_size = 32,
+    quantized_dtype = np.dtype([('d', '<f2'), ('qs', 'i1', (32,))]))
+
+# Quantized types skipped here because they may also map to np.float32
+NUMPY_TYPE_TO_DATA_TYPE: Dict['np.dtype[Any]', DataType] = {}
+for dt in (DT_BF16, DT_F16, DT_F32, DT_I32):
+    if dt.dtype in NUMPY_TYPE_TO_DATA_TYPE:
+        raise ValueError(f'Invalid duplicate data type {dt}')
+    NUMPY_TYPE_TO_DATA_TYPE[dt.dtype] = dt

 SAFETENSORS_DATA_TYPES: Dict[str, DataType] = {
    'BF16': DT_BF16,
@@ -73,20 +115,22 @@ SAFETENSORS_DATA_TYPES: Dict[str, DataType] = {
 # TODO: rename to LLAMAFileType
 # TODO: move to `gguf.py`
 class GGMLFileType(enum.IntEnum):
-    AllF32    = 0
-    MostlyF16 = 1  # except 1d tensors
+    AllF32     = 0
+    MostlyF16  = 1  # except 1d tensors
+    MostlyQ8_0 = 7  # except 1d tensors

    def type_for_tensor(self, name: str, tensor: 'LazyTensor') -> DataType:
-        if len(tensor.shape) == 1:
-            # 1D tensors are always F32.
-            return DT_F32
-        elif self == GGMLFileType.AllF32:
-            return DT_F32
-        elif self == GGMLFileType.MostlyF16:
-            return DT_F16
-        else:
+        dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self)
+        if dt is None:
            raise ValueError(self)
+        # 1D tensors are always F32.
+        return dt if len(tensor.shape) > 1 else DT_F32

+GGML_FILE_TYPE_TO_DATA_TYPE: Dict[GGMLFileType, DataType] = {
+    GGMLFileType.AllF32    : DT_F32,
+    GGMLFileType.MostlyF16 : DT_F16,
+    GGMLFileType.MostlyQ8_0: DT_Q8_0,
+}

 #
 # hparams loading
@@ -170,7 +214,8 @@ class Params:
        f_norm_eps       = config["rms_norm_eps"]
        f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None

-        if "rope_scaling" in config and config["rope_scaling"].get("type") == "linear":
+        rope_scaling = config.get("rope_scaling")
+        if isinstance(rope_scaling, dict) and rope_scaling.get("type") == "linear":
            f_rope_scale = config["rope_scaling"].get("factor")
        else:
            f_rope_scale = None
@@ -414,7 +459,7 @@ class UnquantizedTensor(Tensor):
        self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype]

    def astype(self, data_type: DataType) -> Tensor:
-        dtype = DATA_TYPE_TO_NUMPY[data_type]
+        dtype = data_type.dtype
        if self.data_type == DT_BF16:
            self.ndarray = bf16_to_fp32(self.ndarray)
        return UnquantizedTensor(self.ndarray.astype(dtype))
@@ -453,22 +498,6 @@ def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, conv
 GGMLCompatibleTensor = Union[UnquantizedTensor]


-class DeferredPermutedTensor(Tensor):
-    def __init__(self, base: Tensor, n_head: int, n_head_kv: int) -> None:
-        self.base = base
-        self.n_head = n_head
-        self.data_type = self.base.data_type
-
-    def astype(self, data_type: DataType) -> Tensor:
-        return self.base.astype(data_type).permute(self.n_head, self.n_head_kv)
-
-    def to_ggml(self) -> GGMLCompatibleTensor:
-        return self.base.to_ggml().permute(self.n_head, self.n_head_kv)
-
-    def permute(self, n_head: int, n_head_kv: int) -> Tensor:
-        raise Exception("shouldn't permute twice")
-
-
@dataclass
 class LazyTensor:
    _load: Callable[[], Tensor]
@@ -478,7 +507,9 @@ class LazyTensor:

    def load(self) -> Tensor:
        ret = self._load()
-        assert ret.data_type == self.data_type, (self.data_type, ret.data_type, self.description)
+        # Should be okay if it maps to the same numpy type?
+        assert ret.data_type == self.data_type or (self.data_type.dtype == ret.data_type.dtype), \
+                (self.data_type, ret.data_type, self.description)
        return ret

    def astype(self, data_type: DataType) -> 'LazyTensor':
@@ -489,8 +520,8 @@ class LazyTensor:
        return LazyTensor(load, self.shape, data_type, f'convert({data_type}) {self.description}')

    def validate_conversion_to(self, data_type: DataType) -> None:
-        if data_type == self.data_type:
-            return
+        if data_type != self.data_type and data_type.name not in self.data_type.valid_conversions:
+            raise ValueError(f'Cannot validate conversion from {self.data_type} to {data_type}.')


 LazyModel = Dict[str, LazyTensor]
@@ -616,9 +647,7 @@ class LazyUnpickler(pickle.Unpickler):
        info = self.zip_file.getinfo(filename)

        def load(offset: int, elm_count: int) -> NDArray:
-            dtype = DATA_TYPE_TO_NUMPY.get(data_type)
-            if dtype is None:
-                raise Exception("tensor stored in unsupported format")
+            dtype = data_type.dtype
            fp = self.zip_file.open(info)
            fp.seek(offset * dtype.itemsize)
            size = elm_count * dtype.itemsize
@@ -682,7 +711,7 @@ def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus:

    def convert(info: Dict[str, Any]) -> LazyTensor:
        data_type = SAFETENSORS_DATA_TYPES[info['dtype']]
-        numpy_dtype = DATA_TYPE_TO_NUMPY[data_type]
+        numpy_dtype = data_type.dtype
        shape: List[int] = info['shape']
        begin, end = info['data_offsets']
        assert 0 <= begin <= end <= len(byte_buf)
@@ -722,23 +751,35 @@ def lazy_load_file(path: Path) -> ModelPlus:
 In = TypeVar('In')
 Out = TypeVar('Out')

-def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int) -> Iterable[Out]:
+def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int, max_workers: Optional[int] = None, factory: Callable = ThreadPoolExecutor) -> Iterable[Out]:
    '''Parallel map, but with backpressure.  If the caller doesn't call `next`
    fast enough, this will stop calling `func` at some point rather than
    letting results pile up in memory.  Specifically, there is a max of one
    output value buffered per thread.'''
-    with concurrent.futures.ThreadPoolExecutor() as executor:
+    if concurrency < 2:
+        yield from map(func, iterable)
+        # Not reached.
+    iterable = iter(iterable)
+    with factory(max_workers = max_workers) as executor:
        futures: List[concurrent.futures.Future[Out]] = []
-        items_rev = list(iterable)[::-1]
-        for i in range(min(concurrency, len(items_rev))):
-            futures.append(executor.submit(func, items_rev.pop()))
+        done = False
+        for _ in range(concurrency):
+            try:
+                futures.append(executor.submit(func, next(iterable)))
+            except StopIteration:
+                done = True
+                break
+
        while futures:
            result = futures.pop(0).result()
-            if items_rev:
-                futures.append(executor.submit(func, items_rev.pop()))
+            while not done and len(futures) < concurrency:
+                try:
+                    futures.append(executor.submit(func, next(iterable)))
+                except StopIteration:
+                    done = True
+                    break
            yield result

-
 def check_vocab_size(params: Params, vocab: Vocab) -> None:
    if params.n_vocab != vocab.vocab_size:
        assert isinstance(vocab, BpeVocab) or isinstance(vocab, SentencePieceVocab)
@@ -803,12 +844,11 @@ class OutputFile:
        self.gguf.add_token_types(toktypes)

    def add_tensor_info(self, name: str, tensor: LazyTensor) -> None:
-        n_elements = 1
-        for dim in tensor.shape:
-            n_elements *= dim
-        data_type = DATA_TYPE_TO_NUMPY[tensor.data_type]
-        data_nbytes = n_elements * data_type.itemsize
-        self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes)
+        n_elements = int(np.prod(tensor.shape))
+        raw_dtype = getattr(tensor.data_type, 'ggml_type', None)
+        data_type = getattr(tensor.data_type, 'quantized_type', None) or tensor.data_type.dtype
+        data_nbytes = tensor.data_type.elements_to_bytes(n_elements)
+        self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes, raw_dtype = raw_dtype)

    def write_meta(self) -> None:
        self.gguf.write_header_to_file()
@@ -834,7 +874,20 @@ class OutputFile:
        of.close()

    @staticmethod
-    def write_all(fname_out: Path, params: Params, model: LazyModel, vocab: Vocab) -> None:
+    def do_item(item: Tuple[str, LazyTensor]) -> Tuple[DataType, NDArray]:
+        name, lazy_tensor = item
+        tensor = lazy_tensor.load().to_ggml()
+        return (lazy_tensor.data_type, tensor.ndarray)
+
+    @staticmethod
+    def maybe_do_quantize(item: Tuple[DataType, NDArray]) -> NDArray:
+        dt, arr = item
+        if not isinstance(dt, QuantizedDataType):
+            return arr
+        return dt.quantize(arr)
+
+    @staticmethod
+    def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, concurrency: int = DEFAULT_CONCURRENCY) -> None:
        check_vocab_size(params, vocab)

        of = OutputFile(fname_out)
@@ -850,16 +903,19 @@ class OutputFile:
        of.write_meta()
        of.write_tensor_info()

-        def do_item(item: Tuple[str, LazyTensor]) -> NDArray:
-            name, lazy_tensor = item
-            return lazy_tensor.load().to_ggml().ndarray
-
        # tensor data
-        ndarrays = bounded_parallel_map(do_item, model.items(), concurrency=8)
+        ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency = concurrency)
+        if ftype == GGMLFileType.MostlyQ8_0:
+            ndarrays = bounded_parallel_map(OutputFile.maybe_do_quantize, ndarrays_inner, concurrency = concurrency, max_workers = concurrency, factory = ProcessPoolExecutor)
+        else:
+            ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)
+
+        start = time.time()
        for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
+            elapsed = time.time() - start
            size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
            padi = len(str(len(model)))
-            print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type}")
+            print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}")
            of.gguf.write_tensor_data(ndarray)

        of.close()
@@ -871,6 +927,8 @@ def pick_output_type(model: LazyModel, output_type_str: Optional[str]) -> GGMLFi
        return GGMLFileType.AllF32
    if output_type_str == "f16" or (output_type_str is None and wq_type in (DT_F16, DT_BF16)):
        return GGMLFileType.MostlyF16
+    if output_type_str == "q8_0":
+        return GGMLFileType.MostlyQ8_0

    name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()}

@@ -917,7 +975,7 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
            print(f"skipping tensor {name_new}")
            continue
        else:
-            print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type} | {lazy_tensor.shape}")
+            print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}")
            out[name_new] = lazy_tensor

    return out
@@ -1022,6 +1080,7 @@ def default_outfile(model_paths: List[Path], file_type: GGMLFileType) -> Path:
    namestr = {
        GGMLFileType.AllF32:    "f32",
        GGMLFileType.MostlyF16: "f16",
+        GGMLFileType.MostlyQ8_0:"q8_0",
    }[file_type]
    ret = model_paths[0].parent / f"ggml-model-{namestr}.gguf"
    if ret in model_paths:
@@ -1045,12 +1104,13 @@ def main(args_in: Optional[List[str]] = None) -> None:
    parser.add_argument("--dump",        action="store_true",    help="don't convert, just show what's in the model")
    parser.add_argument("--dump-single", action="store_true",    help="don't convert, just show what's in a single model file")
    parser.add_argument("--vocab-only",  action="store_true",    help="extract only the vocab")
-    parser.add_argument("--outtype",     choices=["f32", "f16"], help="output format (default: based on input)")
+    parser.add_argument("--outtype",     choices=["f32", "f16", "q8_0"], help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
    parser.add_argument("--vocab-dir",   type=Path,              help="directory containing tokenizer.model, if separate from model file")
    parser.add_argument("--outfile",     type=Path,              help="path to write to; default: based on input")
    parser.add_argument("model",         type=Path,              help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
    parser.add_argument("--vocabtype",   choices=["spm", "bpe"], help="vocab format (default: spm)", default="spm")
    parser.add_argument("--ctx",         type=int,               help="model training context (default: based on input)")
+    parser.add_argument("--concurrency", type=int,               help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default = DEFAULT_CONCURRENCY)
    args = parser.parse_args(args_in)

    if args.dump_single:
@@ -1072,6 +1132,7 @@ def main(args_in: Optional[List[str]] = None) -> None:
        params.ftype = {
            "f32": GGMLFileType.AllF32,
            "f16": GGMLFileType.MostlyF16,
+            "q8_0": GGMLFileType.MostlyQ8_0,
        }[args.outtype]

    print(f"params = {params}")
@@ -1103,7 +1164,7 @@ def main(args_in: Optional[List[str]] = None) -> None:
        params.ftype = ftype
        print(f"Writing {outfile}, format {ftype}")

-        OutputFile.write_all(outfile, params, model, vocab)
+        OutputFile.write_all(outfile, ftype, params, model, vocab, concurrency = args.concurrency)
        print(f"Wrote {outfile}")


--- a/examples/beam_search/beam_search.cpp
+++ b/examples/beam_search/beam_search.cpp
@@ -35,7 +35,7 @@ struct ostream_beam_view {
 std::ostream& operator<<(std::ostream& os, const ostream_beam_view & obv) {
    os << "p(" << obv.beam_view.p << ") eob(" << std::boolalpha << obv.beam_view.eob << ") tokens(";
    for (size_t i = 0 ; i < obv.beam_view.n_tokens ; ++i) {
-        os << llama_token_to_str(obv.ctx, obv.beam_view.tokens[i]);
+        os << llama_token_to_piece(obv.ctx, obv.beam_view.tokens[i]);
    }
    return os << ')';
 }
@@ -156,7 +156,7 @@ int main(int argc, char ** argv)

    for( auto id : tokens_list )
    {
-        std::cout << llama_token_to_str(ctx, id);
+        std::cout << llama_token_to_piece(ctx, id);
    }
    std::cout << std::flush;

@@ -175,7 +175,7 @@ int main(int argc, char ** argv)

    std::cout << "\n\n";
    for (llama_token const token_id : callback_data.response) {
-        std::cout << llama_token_to_str(ctx,token_id);
+        std::cout << llama_token_to_piece(ctx,token_id);
    }
    std::cout << std::endl;

--- a/examples/embd-input/embd-input-lib.cpp
+++ b/examples/embd-input/embd-input-lib.cpp
@@ -214,7 +214,7 @@ const char * sampling(struct MyModel * mymodel) {
    if (id == llama_token_eos(ctx)) {
        ret = "</s>";
    } else {
-        ret = llama_token_to_str(ctx, id);
+        ret = llama_token_to_piece(ctx, id);
    }
    eval_id(mymodel, id);
    return ret.c_str();
--- a/examples/embedding/embedding.cpp
+++ b/examples/embedding/embedding.cpp
@@ -56,9 +56,6 @@ int main(int argc, char ** argv) {

    int n_past = 0;

-    // Add a space in front of the first character to match OG llama tokenizer behavior
-    params.prompt.insert(0, 1, ' ');
-
    // tokenize the prompt
    auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);

@@ -67,7 +64,7 @@ int main(int argc, char ** argv) {
        fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
        fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
        for (int i = 0; i < (int) embd_inp.size(); i++) {
-            fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]).c_str());
+            fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
        }
        fprintf(stderr, "\n");
    }
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -189,12 +189,14 @@ int main(int argc, char ** argv) {
        }
    }

-    const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
+    // Add BOS if SPM tokenizer
+    const bool add_bos = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;

    // tokenize the prompt
    std::vector<llama_token> embd_inp;
+
    if (params.interactive_first || params.instruct || !params.prompt.empty() || session_tokens.empty()) {
-        embd_inp = ::llama_tokenize(ctx, params.prompt, is_spm);
+        embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
    } else {
        embd_inp = session_tokens;
    }
@@ -209,10 +211,9 @@ int main(int argc, char ** argv) {
    int guidance_offset = 0;
    int original_prompt_len = 0;
    if (ctx_guidance) {
-        params.cfg_negative_prompt.insert(0, 1, ' ');
-        guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, is_spm);
+        guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, add_bos);

-        std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, is_spm);
+        std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
        original_prompt_len = original_inp.size();
        guidance_offset = (int)guidance_inp.size() - original_prompt_len;
    }
@@ -259,7 +260,7 @@ int main(int argc, char ** argv) {
    }

    // prefix & suffix for instruct mode
-    const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", is_spm);
+    const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos);
    const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n",    false);

    // in instruct mode, we inject a prefix and a suffix to each input by the user
@@ -278,7 +279,7 @@ int main(int argc, char ** argv) {
        fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
        fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
        for (int i = 0; i < (int) embd_inp.size(); i++) {
-            fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]).c_str());
+            fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
        }

        if (ctx_guidance) {
@@ -286,14 +287,14 @@ int main(int argc, char ** argv) {
            fprintf(stderr, "%s: negative prompt: '%s'\n", __func__, params.cfg_negative_prompt.c_str());
            fprintf(stderr, "%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size());
            for (int i = 0; i < (int) guidance_inp.size(); i++) {
-                fprintf(stderr, "%6d -> '%s'\n", guidance_inp[i], llama_token_to_str(ctx, guidance_inp[i]).c_str());
+                fprintf(stderr, "%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str());
            }
        }

        if (params.n_keep > 0) {
        fprintf(stderr, "%s: static prompt based on n_keep: '", __func__);
            for (int i = 0; i < params.n_keep; i++) {
-                fprintf(stderr, "%s", llama_token_to_str(ctx, embd_inp[i]).c_str());
+                fprintf(stderr, "%s", llama_token_to_piece(ctx, embd_inp[i]).c_str());
            }
            fprintf(stderr, "'\n");
        }
@@ -449,7 +450,7 @@ int main(int argc, char ** argv) {
                //printf("\n---\n");
                //printf("resetting: '");
                //for (int i = 0; i < (int) embd.size(); i++) {
-                //    printf("%s", llama_token_to_str(ctx, embd[i]));
+                //    printf("%s", llama_token_to_piece(ctx, embd[i]));
                //}
                //printf("'\n");
                //printf("\n---\n");
@@ -502,7 +503,7 @@ int main(int argc, char ** argv) {
                    input_size = embd_guidance.size();
                    //fprintf(stderr, "\n---------------------\n");
                    //for (int i = 0; i < (int) embd_guidance.size(); i++) {
-                        //fprintf(stderr, "%s", llama_token_to_str(ctx, embd_guidance[i]));
+                        //fprintf(stderr, "%s", llama_token_to_piece(ctx, embd_guidance[i]));
                    //}
                    //fprintf(stderr, "\n---------------------\n");
                } else {
@@ -597,7 +598,12 @@ int main(int argc, char ** argv) {
                    last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
                    last_n_repeat, alpha_frequency, alpha_presence);
                if (!penalize_nl) {
-                    logits[llama_token_nl(ctx)] = nl_logit;
+                    for (size_t idx = 0; idx < candidates_p.size; idx++) {
+                        if (candidates_p.data[idx].id == llama_token_nl(ctx)) {
+                            candidates_p.data[idx].logit = nl_logit;
+                            break;
+                        }
+                    }
                }

                if (grammar != NULL) {
@@ -661,7 +667,7 @@ int main(int argc, char ** argv) {
        // display text
        if (input_echo) {
            for (auto id : embd) {
-                printf("%s", llama_token_to_str(ctx, id).c_str());
+                printf("%s", llama_token_to_piece(ctx, id).c_str());
            }
            fflush(stdout);
        }
@@ -677,7 +683,7 @@ int main(int argc, char ** argv) {
            if (params.antiprompt.size()) {
                std::string last_output;
                for (auto id : last_n_tokens) {
-                    last_output += llama_token_to_str(ctx, id);
+                    last_output += llama_token_to_piece(ctx, id);
                }

                is_antiprompt = false;
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -351,6 +351,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
    fprintf(stderr, "%s : loaded %zu tasks from prompt.\n", __func__, hs_task_count);

    const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
+    fprintf(stderr, "================================= is_spm = %d\n", is_spm);

    // This is needed as usual for LLaMA models
    const bool add_bos = is_spm;
@@ -391,7 +392,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
        hs_data[i].context = prompt_lines[idx*6];
        hs_data[i].gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
        for (size_t j=0; j < 4; j++) {
-            hs_data[i].ending[j] = " " + prompt_lines[idx*6+2+j];
+            hs_data[i].ending[j] = prompt_lines[idx*6+2+j];
        }

        // Delete the selected random example from the prompt
@@ -406,6 +407,8 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
    double acc = 0.0f;
    const int n_vocab = llama_n_vocab(ctx);

+    std::vector<std::vector<int>> ending_tokens(4);
+
    std::vector<float> tok_logits(n_vocab);

    for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) {
@@ -413,11 +416,21 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
        std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, add_bos);
        size_t context_size = context_embd.size();

+        for (int i = 0; i < 4; ++i) {
+            ending_tokens[i] = ::llama_tokenize(ctx, hs_data[task_idx].context + " " + hs_data[task_idx].ending[i], add_bos);
+            for (int k = 0; k < int(context_size); ++k) {
+                if (ending_tokens[i][k] != context_embd[k]) {
+                    fprintf(stderr, "Oops: ending %d of task %d differs from context at position %d\n",i,int(task_idx),k);
+                    break;
+                }
+            }
+        }
+
        // Do the 1st ending
        // In this case we include the context when evaluating
-        auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], add_bos);
+        //auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], add_bos);
+        auto query_embd = ending_tokens[0];
        auto query_size = query_embd.size();
-        //printf("First query: %d\n",(int)query_size);

        // Stop if query wont fit the ctx window
        if (query_size > (size_t)params.n_ctx) {
@@ -462,7 +475,8 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
        for (size_t ending_idx = 1; ending_idx < 4; ending_idx++) {

            // Tokenize the query
-            query_embd = ::llama_tokenize(ctx, hs_data[task_idx].ending[ending_idx], false);
+            query_embd.resize(ending_tokens[ending_idx].size() - context_size);
+            std::memcpy(query_embd.data(), ending_tokens[ending_idx].data() + context_size, query_embd.size()*sizeof(int));
            query_size = query_embd.size();

            // Stop if query wont fit the ctx window
--- a/examples/save-load-state/save-load-state.cpp
+++ b/examples/save-load-state/save-load-state.cpp
@@ -87,7 +87,7 @@ int main(int argc, char ** argv) {
        }
        llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
        auto next_token = llama_sample_token(ctx, &candidates_p);
-        auto next_token_str = llama_token_to_str(ctx, next_token);
+        auto next_token_str = llama_token_to_piece(ctx, next_token);
        last_n_tokens_data.push_back(next_token);

        printf("%s", next_token_str.c_str());
@@ -147,7 +147,7 @@ int main(int argc, char ** argv) {
        }
        llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
        auto next_token = llama_sample_token(ctx2, &candidates_p);
-        auto next_token_str = llama_token_to_str(ctx2, next_token);
+        auto next_token_str = llama_token_to_piece(ctx2, next_token);
        last_n_tokens_data.push_back(next_token);

        printf("%s", next_token_str.c_str());
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -77,34 +77,31 @@ You need to have [Node.js](https://nodejs.org/en) installed.
 ```bash
 mkdir llama-client
 cd llama-client
-npm init
-npm install axios
 ```

 Create a index.js file and put inside this:

 ```javascript
-const axios = require("axios");
-
 const prompt = `Building a website can be done in 10 simple steps:`;

 async function Test() {
-    let result = await axios.post("http://127.0.0.1:8080/completion", {
-        prompt,
-        n_predict: 512,
-    });
-
-    // the response is received until completion finish
-    console.log(result.data.content);
+    let response = await fetch("http://127.0.0.1:8080/completion", {
+        method: 'POST',
+        body: JSON.stringify({
+            prompt,
+            n_predict: 512,
+        })
+    })
+    console.log((await response.json()).content)
 }

-Test();
+Test()
 ```

 And run it:

 ```bash
-node .
+node index.js
 ```

 ## API Endpoints
@@ -167,6 +164,12 @@ node .

    Note that the special `BOS` token is not added in front of the text and also a space character is not inserted automatically as it is for `/completion`.

+-   **POST** `/detokenize`: Convert tokens to text.
+
+    *Options:*
+
+    `tokens`: Set the tokens to detokenize.
+
 -   **POST** `/embedding`: Generate embedding of a given text just as [the embedding example](../embedding) does.

    *Options:*
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -94,7 +94,7 @@ static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end)
    std::string ret;
    for (; begin != end; ++begin)
    {
-        ret += llama_token_to_str(ctx, *begin);
+        ret += llama_token_to_piece(ctx, *begin);
    }
    return ret;
 }
@@ -123,7 +123,7 @@ static void server_log(const char *level, const char *function, int line,
 // format incomplete utf-8 multibyte character for output
 static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token)
 {
-    std::string out = token == -1 ? "" : llama_token_to_str(ctx, token);
+    std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token);
    // if the size is 1 and first bit is 1, meaning it's a partial character
    //   (size > 1 meaning it's already a known token)
    if (out.size() == 1 && (out[0] & 0x80) == 0x80)
@@ -286,7 +286,6 @@ struct llama_server_context
                    std::vector<llama_token> p;
                    if (first)
                    {
-                        s.insert(0, 1, ' '); // add a space if it's the first
                        p = ::llama_tokenize(ctx, s, add_bos);
                        first = false;
                    }
@@ -309,7 +308,6 @@ struct llama_server_context
        else
        {
            auto s = json_prompt.template get<std::string>();
-            s.insert(0, 1, ' '); // always add a first space
            prompt_tokens = ::llama_tokenize(ctx, s, add_bos);
        }

@@ -566,7 +564,7 @@ struct llama_server_context

        if (!embd.empty() && embd.back() == llama_token_eos(ctx))
        {
-            // stopping_word = llama_token_to_str(ctx, embd.back());
+            // stopping_word = llama_token_to_piece(ctx, embd.back());
            has_next_token = false;
            stopped_eos = true;
            LOG_VERBOSE("eos token found", {});
@@ -613,7 +611,7 @@ struct llama_server_context
    {
        const completion_token_output token_with_probs = nextToken();

-        const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(ctx, token_with_probs.tok);
+        const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(ctx, token_with_probs.tok);
        generated_text += token_text;

        if (params.n_probs > 0)
@@ -1104,6 +1102,12 @@ static json format_tokenizer_response(const std::vector<llama_token> &tokens)
        {"tokens", tokens}};
 }

+static json format_detokenized_response(std::string content)
+{
+    return json{
+        {"content", content}};
+}
+
 template <typename T>
 static T json_value(const json &body, const std::string &key, const T &default_value)
 {
@@ -1248,7 +1252,7 @@ void beam_search_callback(void * callback_data, llama_beams_state beams_state) {

 struct token_translator {
    llama_context * ctx;
-    std::string operator()(llama_token tok) const { return llama_token_to_str(ctx, tok); }
+    std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); }
    std::string operator()(completion_token_output cto) const { return (*this)(cto.tok); }
 };

@@ -1358,7 +1362,7 @@ int main(int argc, char **argv)

                while (llama.has_next_token) {
                    const completion_token_output token_with_probs = llama.doCompletion();
-                    const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(llama.ctx, token_with_probs.tok);
+                    const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(llama.ctx, token_with_probs.tok);

                    stop_pos = llama.findStoppingStrings(llama.generated_text,
                        token_text.size(), STOP_FULL);
@@ -1389,7 +1393,7 @@ int main(int argc, char **argv)
                    if (token_with_probs.tok == -1 || llama.multibyte_pending > 0) {
                        continue;
                    }
-                    const std::string token_text = llama_token_to_str(llama.ctx, token_with_probs.tok);
+                    const std::string token_text = llama_token_to_piece(llama.ctx, token_with_probs.tok);

                    size_t pos = std::min(sent_count, llama.generated_text.size());

@@ -1501,6 +1505,21 @@ int main(int argc, char **argv)
        const json data = format_tokenizer_response(tokens);
        return res.set_content(data.dump(), "application/json"); });

+    svr.Post("/detokenize", [&llama](const Request &req, Response &res)
+             {
+        auto lock = llama.lock();
+
+        const json body = json::parse(req.body);
+        std::string content;
+        if (body.count("tokens") != 0)
+        {
+            const std::vector<llama_token> tokens = body["tokens"];
+            content = tokens_to_str(llama.ctx, tokens.cbegin(), tokens.cend());
+        }
+
+        const json data = format_detokenized_response(content);
+        return res.set_content(data.dump(), "application/json"); });
+
    svr.Post("/embedding", [&llama](const Request &req, Response &res)
             {
        auto lock = llama.lock();
--- a/examples/simple/simple.cpp
+++ b/examples/simple/simple.cpp
@@ -63,7 +63,7 @@ int main(int argc, char ** argv) {
    fprintf(stderr, "\n\n");

    for (auto id : tokens_list) {
-        fprintf(stderr, "%s", llama_token_to_str(ctx, id).c_str());
+        fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
    }

    fflush(stderr);
@@ -112,7 +112,7 @@ int main(int argc, char ** argv) {
        }

        // print the new token :
-        printf("%s", llama_token_to_str(ctx, new_token_id).c_str());
+        printf("%s", llama_token_to_piece(ctx, new_token_id).c_str());
        fflush(stdout);

        // push this new token for next evaluation
--- a/examples/train-text-from-scratch/train-text-from-scratch.cpp
+++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp
@@ -1964,7 +1964,7 @@ void print_matrix(struct ggml_tensor * probs) {


 void print_token(struct llama_context * ctx, llama_token token) {
-    printf("%s", llama_token_to_str(ctx, token).c_str());
+    printf("%s", llama_token_to_piece(ctx, token).c_str());
 }

 void print_tokens(struct llama_context* ctx, struct ggml_tensor * tokens) {
@@ -2202,7 +2202,7 @@ int tokenize_file(struct llama_context * lctx, const char * filename, std::vecto
        const char * in  = buf.data();
        const char * end = buf.data() + buf.size();
        for (int i = 0; i < (int) out.size(); ++i) {
-            std::string s = llama_token_to_str(lctx, out[i]);
+            std::string s = llama_token_to_piece(lctx, out[i]);
            int len = s.length();
            if (in >= end) {
                printf("%s: unexpected end of original text.\n", __func__);
--- a/flake.lock
+++ b/flake.lock
@@ -5,11 +5,11 @@
        "systems": "systems"
      },
      "locked": {
-        "lastModified": 1685518550,
-        "narHash": "sha256-o2d0KcvaXzTrPRIo0kOLV0/QXHhDQ5DTi+OxcjO8xqY=",
+        "lastModified": 1692799911,
+        "narHash": "sha256-3eihraek4qL744EvQXsK1Ha6C3CR7nnT8X2qWap4RNk=",
        "owner": "numtide",
        "repo": "flake-utils",
-        "rev": "a1720a10a6cfe8234c0e93907ffe81be440f4cef",
+        "rev": "f9e7cf818399d17d347f847525c5a5a8032e4e44",
        "type": "github"
      },
      "original": {
@@ -20,11 +20,11 @@
    },
    "nixpkgs": {
      "locked": {
-        "lastModified": 1685931219,
-        "narHash": "sha256-8EWeOZ6LKQfgAjB/USffUSELPRjw88A+xTcXnOUvO5M=",
+        "lastModified": 1692913444,
+        "narHash": "sha256-1SvMQm2DwofNxXVtNWWtIcTh7GctEVrS/Xel/mdc6iY=",
        "owner": "NixOS",
        "repo": "nixpkgs",
-        "rev": "7409480d5c8584a1a83c422530419efe4afb0d19",
+        "rev": "18324978d632ffc55ef1d928e81630c620f4f447",
        "type": "github"
      },
      "original": {
--- a/flake.nix
+++ b/flake.nix
@@ -6,6 +6,9 @@
  outputs = { self, nixpkgs, flake-utils }:
    flake-utils.lib.eachDefaultSystem (system:
      let
+        name = "llama.cpp";
+        src = ./.;
+        meta.mainProgram = "llama";
        inherit (pkgs.stdenv) isAarch32 isAarch64 isDarwin;
        buildInputs = with pkgs; [ openmpi ];
        osSpecific = with pkgs; buildInputs ++
@@ -21,11 +24,17 @@
              CoreGraphics
              CoreVideo
            ]
+          else if isDarwin then
+            with pkgs.darwin.apple_sdk.frameworks; [
+              Accelerate
+              CoreGraphics
+              CoreVideo
+            ]
          else
            with pkgs; [ openblas ]
        );
        pkgs = import nixpkgs { inherit system; };
-        nativeBuildInputs = with pkgs; [ cmake pkgconfig ];
+        nativeBuildInputs = with pkgs; [ cmake ninja pkgconfig ];
        llama-python =
          pkgs.python3.withPackages (ps: with ps; [ numpy sentencepiece ]);
        postPatch = ''
@@ -38,35 +47,35 @@
          mv $out/bin/server $out/bin/llama-server
        '';
        cmakeFlags = [ "-DLLAMA_BUILD_SERVER=ON" "-DLLAMA_MPI=ON" "-DBUILD_SHARED_LIBS=ON" "-DCMAKE_SKIP_BUILD_RPATH=ON" ];
-      in {
+      in
+      {
        packages.default = pkgs.stdenv.mkDerivation {
-          name = "llama.cpp";
-          src = ./.;
-          postPatch = postPatch;
-          nativeBuildInputs = nativeBuildInputs;
-          buildInputs = osSpecific;
+          inherit name src meta postPatch nativeBuildInputs buildInputs postInstall;
          cmakeFlags = cmakeFlags
            ++ (if isAarch64 && isDarwin then [
-              "-DCMAKE_C_FLAGS=-D__ARM_FEATURE_DOTPROD=1"
-              "-DLLAMA_METAL=ON"
-            ] else [
-              "-DLLAMA_BLAS=ON"
-              "-DLLAMA_BLAS_VENDOR=OpenBLAS"
+            "-DCMAKE_C_FLAGS=-D__ARM_FEATURE_DOTPROD=1"
+            "-DLLAMA_METAL=ON"
+          ] else [
+            "-DLLAMA_BLAS=ON"
+            "-DLLAMA_BLAS_VENDOR=OpenBLAS"
          ]);
-          postInstall = postInstall;
-          meta.mainProgram = "llama";
        };
        packages.opencl = pkgs.stdenv.mkDerivation {
-          name = "llama.cpp";
-          src = ./.;
-          postPatch = postPatch;
-          nativeBuildInputs = nativeBuildInputs;
+          inherit name src meta postPatch nativeBuildInputs postInstall;
          buildInputs = with pkgs; buildInputs ++ [ clblast ];
          cmakeFlags = cmakeFlags ++ [
            "-DLLAMA_CLBLAST=ON"
          ];
-          postInstall = postInstall;
-          meta.mainProgram = "llama";
+        };
+        packages.rocm = pkgs.stdenv.mkDerivation {
+          inherit name src meta postPatch nativeBuildInputs postInstall;
+          buildInputs = with pkgs; buildInputs ++ [ hip hipblas rocblas ];
+          cmakeFlags = cmakeFlags ++ [
+            "-DLLAMA_HIPBLAS=1"
+            "-DCMAKE_C_COMPILER=hipcc"
+            "-DCMAKE_CXX_COMPILER=hipcc"
+            "-DCMAKE_POSITION_INDEPENDENT_CODE=ON"
+          ];
        };
        apps.llama-server = {
          type = "app";
@@ -80,8 +89,13 @@
          type = "app";
          program = "${self.packages.${system}.default}/bin/llama";
        };
+        apps.quantize = {
+          type = "app";
+          program = "${self.packages.${system}.default}/bin/quantize";
+        };
        apps.default = self.apps.${system}.llama;
        devShells.default = pkgs.mkShell {
+          buildInputs = [ llama-python ];
          packages = nativeBuildInputs ++ osSpecific;
        };
      });
--- a/ggml.c
+++ b/ggml.c
@@ -20516,6 +20516,14 @@ int ggml_cpu_has_sse3(void) {
 #endif
 }

+int ggml_cpu_has_ssse3(void) {
+#if defined(__SSSE3__)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
 int ggml_cpu_has_vsx(void) {
 #if defined(__POWER9_VECTOR__)
    return 1;
--- a/ggml.h
+++ b/ggml.h
@@ -1944,6 +1944,7 @@ extern "C" {
    GGML_API int ggml_cpu_has_clblast    (void);
    GGML_API int ggml_cpu_has_gpublas    (void);
    GGML_API int ggml_cpu_has_sse3       (void);
+    GGML_API int ggml_cpu_has_ssse3      (void);
    GGML_API int ggml_cpu_has_vsx        (void);

    //
--- a/llama.cpp
+++ b/llama.cpp
@@ -1,9 +1,6 @@
 // Defines fileno on msys:
 #ifndef _GNU_SOURCE
 #define _GNU_SOURCE
-#include <cstddef>
-#include <cstdint>
-#include <cstdio>
 #endif

 #include "llama.h"
@@ -62,6 +59,9 @@
 #include <cinttypes>
 #include <climits>
 #include <cstdarg>
+#include <cstddef>
+#include <cstdint>
+#include <cstdio>
 #include <cstring>
 #include <ctime>
 #include <fstream>
@@ -796,12 +796,12 @@ static void llama_nop(struct ggml_tensor * tensor) { // don't offload by default
    (void) tensor;
 }

-static std::string llama_token_to_text(const struct llama_context * ctx, llama_token token) {
+static std::string llama_token_to_str(const struct llama_context * ctx, llama_token token) {
    std::vector<char> result(8, 0);
-    const int n_tokens = llama_token_to_str(ctx, token, result.data(), result.size());
+    const int n_tokens = llama_token_to_piece(ctx, token, result.data(), result.size());
    if (n_tokens < 0) {
        result.resize(-n_tokens);
-        int check = llama_token_to_str(ctx, token, result.data(), result.size());
+        int check = llama_token_to_piece(ctx, token, result.data(), result.size());
        GGML_ASSERT(check == -n_tokens);
    } else {
        result.resize(n_tokens);
@@ -955,10 +955,10 @@ struct llama_vocab {
    id linefeed_id = 13;

    int find_bpe_rank(std::string token_left, std::string token_right) const {
-        replace_all(token_left,  " ",  "Ġ");
-        replace_all(token_left,  "\n", "Ċ");
-        replace_all(token_right, " ",  "Ġ");
-        replace_all(token_right, "\n", "Ċ");
+        replace_all(token_left,  " ",  "\u0120");
+        replace_all(token_left,  "\n", "\u010A");
+        replace_all(token_right, " ",  "\u0120");
+        replace_all(token_right, "\n", "\u010A");

        auto it = bpe_ranks.find(std::make_pair(token_left, token_right));
        if (it == bpe_ranks.end()) {
@@ -1635,7 +1635,8 @@ static void llm_load_hparams(
 }

 // TODO: This should probably be in llama.h
-static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, const std::string & raw_text, bool bos, bool escape);
+static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos);
+static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch);

 static void llm_load_vocab(
        llama_model_loader & ml,
@@ -1737,7 +1738,11 @@ static void llm_load_vocab(
    }

    // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
-    vocab.linefeed_id = llama_tokenize_internal(vocab, "\n", false, false)[0];
+    if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
+        vocab.linefeed_id = llama_byte_to_token(vocab, '\n');
+    } else {
+        vocab.linefeed_id = llama_tokenize_internal(vocab, "\n", false)[0];
+    }

    // special tokens
    GGUF_GET_KEY(ctx, vocab.special_bos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_BOS_ID));
@@ -3026,16 +3031,8 @@ static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
    return vocab.token_to_id.at(buf);
 }

-static std::string llama_escape_whitespace(const std::string& text) {
-    std::string result = "\xe2\x96\x81";
-    for (size_t offs = 0; offs < text.length(); ++offs) {
-        if (text[offs] == ' ') {
-            result += "\xe2\x96\x81";
-        } else {
-            result += text[offs];
-        }
-    }
-    return result;
+static void llama_escape_whitespace(std::string & text) {
+    replace_all(text, " ", "\xe2\x96\x81");
 }

 static void llama_unescape_whitespace(std::string & word) {
@@ -3219,7 +3216,7 @@ struct llm_bigram_bpe {
 };

 struct llm_tokenizer_bpe {
-    llm_tokenizer_bpe(const llama_vocab & vocab, bool g2ws): vocab(vocab) { flag_g2ws = g2ws; }
+    llm_tokenizer_bpe(const llama_vocab & vocab): vocab(vocab) {}

    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
        int final_prev_index = -1;
@@ -3371,8 +3368,6 @@ private:
        return words;
    }

-    bool flag_g2ws = false;
-
    const llama_vocab & vocab;

    std::vector<llm_symbol> symbols;
@@ -3381,9 +3376,18 @@ private:
    llm_bigram_bpe::queue work_queue;
 };

-static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, const std::string & raw_text, bool bos, bool escape) {
+static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos) {
    std::vector<llama_vocab::id> output;

+    // OG tokenizer behavior:
+    //
+    // tokenizer.encode('', add_bos=True)  returns [1]
+    // tokenizer.encode('', add_bos=False) returns []
+
+    if (bos && vocab.special_bos_id != -1) {
+        output.push_back(vocab.special_bos_id);
+    }
+
    if (raw_text.empty()) {
        return output;
    }
@@ -3391,29 +3395,16 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
    switch (vocab.type) {
        case LLAMA_VOCAB_TYPE_SPM:
            {
+                // without adding this leading whitespace, we do not get the same results as the original tokenizer
+                raw_text = " " + raw_text;
+
                llm_tokenizer_spm tokenizer(vocab);
-
-                if (bos) {
-                    output.push_back(vocab.special_bos_id);
-                }
-
-                std::string text;
-                if (escape) {
-                    text = llama_escape_whitespace(raw_text);
-                } else {
-                    text = raw_text;
-                }
-
-                tokenizer.tokenize(text, output);
+                llama_escape_whitespace(raw_text);
+                tokenizer.tokenize(raw_text, output);
            } break;
        case LLAMA_VOCAB_TYPE_BPE:
            {
-                llm_tokenizer_bpe tokenizer(vocab, escape);
-
-                if (bos && vocab.special_bos_id != -1) {
-                    output.push_back(vocab.special_bos_id);
-                }
-
+                llm_tokenizer_bpe tokenizer(vocab);
                tokenizer.tokenize(raw_text, output);
            } break;
    };
@@ -3908,7 +3899,7 @@ void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array *

    // Calculate absolute value of second derivatives
    for (size_t i = 0; i < second_derivatives.size(); ++i) {
-        second_derivatives[i] = abs(second_derivatives[i]);
+        second_derivatives[i] = std::abs(second_derivatives[i]);
    }

    // Normalize the second derivatives
@@ -4099,16 +4090,16 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
    std::vector<llama_grammar_candidate>                              candidates_grammar;

    for (size_t i = 0; i < candidates->size; ++i) {
-        const llama_token id   = candidates->data[i].id;
-        const std::string text = llama_token_to_text(ctx, id);
+        const llama_token id    = candidates->data[i].id;
+        const std::string piece = llama_token_to_str(ctx, id);
        if (id == eos) {
            if (!allow_eos) {
                candidates->data[i].logit = -INFINITY;
            }
-        } else if (text.empty() || text[0] == 0) {
+        } else if (piece.empty() || piece[0] == 0) {
            candidates->data[i].logit = -INFINITY;
        } else {
-            candidates_decoded.push_back(decode_utf8(text.c_str(), grammar->partial_utf8));
+            candidates_decoded.push_back(decode_utf8(piece.c_str(), grammar->partial_utf8));
            candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second });
        }
    }
@@ -4312,10 +4303,10 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
        GGML_ASSERT(false);
    }

-    const std::string text = llama_token_to_text(ctx, token);
+    const std::string piece = llama_token_to_str(ctx, token);

    // Note terminating 0 in decoded string
-    const auto   decoded     = decode_utf8(text.c_str(), grammar->partial_utf8);
+    const auto   decoded     = decode_utf8(piece.c_str(), grammar->partial_utf8);
    const auto & code_points = decoded.first;
    for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
        grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it);
@@ -4674,6 +4665,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s

    std::unique_ptr<llama_model_loader> ml(new llama_model_loader(fname_inp, /*use_mmap*/ false));

+    llama_model model;
+    llm_load_arch(*ml, model);
+    llm_load_hparams(*ml, model, 0, 0, 0);
+
    const size_t align = GGUF_DEFAULT_ALIGNMENT;
    struct gguf_context * ctx_out = gguf_init_empty();

@@ -4699,6 +4694,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
            ++n_feed_forward_w2;
        }
    }
+    if (n_attention_wv != n_feed_forward_w2 || (uint32_t)n_attention_wv != model.hparams.n_layer) {
+        LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_feed_forward_w2 = %d, hparams.n_layer = %d\n",
+                __func__, n_attention_wv, n_feed_forward_w2, model.hparams.n_layer);
+    }

    int i_attention_wv = 0;
    int i_feed_forward_w2 = 0;
@@ -4775,8 +4774,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s

            if (name == tn(LLM_TENSOR_OUTPUT, "weight")) {
                int nx = tensor->ne[0];
-                int ny = tensor->ne[1];
-                if (nx % QK_K == 0 && ny % QK_K == 0) {
+                if (nx % QK_K == 0) {
                    new_type = GGML_TYPE_Q6_K;
                }
            } else if (name.find("attn_v.weight") != std::string::npos) {
@@ -4790,6 +4788,12 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
                else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
                else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) &&
                        (i_attention_wv < n_attention_wv/8 || i_attention_wv >= 7*n_attention_wv/8)) new_type = GGML_TYPE_Q6_K;
+                if (model.type == MODEL_70B) {
+                    // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
+                    // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
+                    // nearly negligible increase in model size by quantizing this tensor with more bits:
+                    if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K) new_type = GGML_TYPE_Q5_K;
+                }
                ++i_attention_wv;
            } else if (name.find("ffn_down.weight") != std::string::npos) {
                if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
@@ -4819,8 +4823,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
                new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K) {
                int nx = tensor->ne[0];
                int ny = tensor->ne[1];
-                if (nx % QK_K != 0 || ny % QK_K != 0) {
-                    LLAMA_LOG_INFO("\n\nTensor sizes %d x %d are not divisible by %d, required for k-quants.\n",nx,ny,QK_K);
+                if (nx % QK_K != 0) {
+                    LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for k-quants\n", __func__, nx, ny, QK_K);
                    convert_incompatible_tensor = true;
                }
            }
@@ -6095,8 +6099,7 @@ int llama_tokenize_with_model(
                 llama_token * tokens,
                         int   n_max_tokens,
                        bool   add_bos) {
-    auto escape = llama_vocab_get_type(model->vocab) == LLAMA_VOCAB_TYPE_SPM;
-    auto res = llama_tokenize_internal(model->vocab, text, add_bos, escape);
+    auto res = llama_tokenize_internal(model->vocab, text, add_bos);

    if (n_max_tokens < (int) res.size()) {
        LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
@@ -6110,12 +6113,12 @@ int llama_tokenize_with_model(
    return res.size();
 }

-int llama_token_to_str(const struct llama_context * ctx, llama_token token, char * buf, int length) {
-    return llama_token_to_str_with_model(&ctx->model, token, buf, length);
+int llama_token_to_piece(const struct llama_context * ctx, llama_token token, char * buf, int length) {
+    return llama_token_to_piece_with_model(&ctx->model, token, buf, length);
 }

-// does not write null-terminator to str
-int llama_token_to_str_with_model(const struct llama_model * model, llama_token token, char * buf, int length) {
+// does not write null-terminator to buf
+int llama_token_to_piece_with_model(const struct llama_model * model, llama_token token, char * buf, int length) {
    if (0 <= token && token < llama_model_n_vocab(model)) {
        if (llama_is_normal_token(model->vocab, token)) {
            std::string result = model->vocab.id_to_token[token].text;
@@ -6203,6 +6206,7 @@ const char * llama_print_system_info(void) {
    s += "WASM_SIMD = "   + std::to_string(ggml_cpu_has_wasm_simd())   + " | ";
    s += "BLAS = "        + std::to_string(ggml_cpu_has_blas())        + " | ";
    s += "SSE3 = "        + std::to_string(ggml_cpu_has_sse3())        + " | ";
+    s += "SSSE3 = "       + std::to_string(ggml_cpu_has_ssse3())       + " | ";
    s += "VSX = "         + std::to_string(ggml_cpu_has_vsx())         + " | ";

    return s.c_str();
--- a/llama.h
+++ b/llama.h
@@ -381,15 +381,17 @@ extern "C" {
                             int   n_max_tokens,
                            bool   add_bos);

-    // Token Id -> String. Uses the vocabulary in the provided context
-    // Does not write null terminator to the buffer
-    LLAMA_API int llama_token_to_str(
+    // Token Id -> Piece.
+    // Uses the vocabulary in the provided context.
+    // Does not write null terminator to the buffer.
+    // User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
+    LLAMA_API int llama_token_to_piece(
            const struct llama_context * ctx,
                           llama_token   token,
                                  char * buf,
                                  int    length);

-    LLAMA_API int llama_token_to_str_with_model(
+    LLAMA_API int llama_token_to_piece_with_model(
              const struct llama_model * model,
                           llama_token   token,
                                  char * buf,
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -25,8 +25,10 @@ endfunction()
 llama_build_and_test_executable(test-quantize-fns.cpp)
 llama_build_and_test_executable(test-quantize-perf.cpp)
 llama_build_and_test_executable(test-sampling.cpp)
-llama_build_executable(test-tokenizer-0.cpp)
-llama_test_executable (test-tokenizer-0.llama test-tokenizer-0.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
+llama_build_executable(test-tokenizer-0-llama.cpp)
+llama_test_executable (test-tokenizer-0-llama test-tokenizer-0-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
+llama_build_executable(test-tokenizer-0-falcon.cpp)
+#llama_test_executable (test-tokenizer-0-falcon test-tokenizer-0-falcon.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
 llama_build_executable(test-tokenizer-1.cpp)
 # test-tokenizer-1 requires a BPE vocab. re-enable when we have one.
 #llama_test_executable (test-tokenizer-1.llama test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
--- a/tests/test-tokenizer-0-falcon.cpp
+++ b/tests/test-tokenizer-0-falcon.cpp
@@ -0,0 +1,178 @@
+#include "llama.h"
+#include "common.h"
+
+#include <cstdio>
+#include <string>
+#include <map>
+#include <vector>
+#include <fstream>
+
+// generate using test-tokenizer-0-falcon.py
+static const std::map<std::string, std::vector<llama_token>> & k_tests() {
+    static std::map<std::string, std::vector<llama_token>> _k_tests = {
+        { ""                      , {  }, },
+        { " "                     , {     204, }, },
+        { "  "                    , {     258, }, },
+        { "   "                   , {     466, }, },
+        { "\t"                    , {     192, }, },
+        { "\n"                    , {     193, }, },
+        { "\t\n"                  , {   19125, }, },
+        { "Hello world"           , {    9856,   1079, }, },
+        { " Hello world"          , {   23090,   1079, }, },
+        { "Hello World"           , {    9856,   2889, }, },
+        { " Hello World"          , {   23090,   2889, }, },
+        { " Hello World!"         , {   23090,   2889,     12, }, },
+        { "Hello, world!"         , {    9856,     23,   1079,     12, }, },
+        { " Hello, world!"        , {   23090,     23,   1079,     12, }, },
+        { " this is 🦙.cpp"        , {     414,    304,   3346,    111,    231,     25,  29247, }, },
+        { "w048 7tuijk dsdfhu"    , {      98,  55866,    204,     34,  16682,   7149,  36190,   6869,  11481, }, },
+        { "нещо на Български"     , {     150,    133,   6207,    151,    215,    150,    134,   5052,    133,   6279,   5052,    223,    151,    216,  49679,    123,  53110,  47043,   7795, }, },
+        { "កាន់តែពិសេសអាចខលចេញ"   , {   38154,    206,  38154,    126,  38154,    225,    167,    237,    217,  38154,    221,    167,    237,    208,  38154,    228,  38154,    127,  38154,    237,    167,    237,    207,  38154,    237,  38154,    107,  38154,    126,  38154,    211,  38154,    207,  38154,    233,  38154,    211,    167,    237,    207,  38154,    215, }, },
+        { "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", {    2571,    232,    206,    204,     19,  11003,     20,   8196,    126,    283,    219,  48778,    116,  13392,    204,     19,  51831,    732,  63209,   1741,   7955,    522,     20,  22438,    211,    204,     19,   7927,  53360,    325,    504,    701,    946,  10930,     20, }, },
+        { "Hello"                 , {    9856, }, },
+        { " Hello"                , {   23090, }, },
+        { "  Hello"               , {     204,  23090, }, },
+        { "   Hello"              , {     258,  23090, }, },
+        { "    Hello"             , {     466,  23090, }, },
+        { "    Hello\n    Hello"  , {     466,  23090,    742,  23090, }, },
+    };
+
+    return _k_tests;
+}
+
+int main(int argc, char **argv) {
+    if (argc < 2) {
+        fprintf(stderr, "Usage: %s vocab-file [text-file]\n", argv[0]);
+        return 1;
+    }
+
+    const std::string fname = argv[1];
+
+    std::string fname_text;
+    if (argc > 2) {
+        fname_text = argv[2];
+    }
+
+    fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
+
+    llama_model * model;
+    llama_context * ctx;
+
+    llama_backend_init(false);
+
+    // load the vocab
+    {
+        auto lparams = llama_context_default_params();
+
+        lparams.vocab_only = true;
+
+        model = llama_load_model_from_file(fname.c_str(), lparams);
+
+        if (model == NULL) {
+            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
+            return 1;
+        }
+
+        ctx = llama_new_context_with_model(model, lparams);
+
+        if (ctx == NULL) {
+            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
+            llama_free_model(model);
+            return 1;
+        }
+    }
+
+    if (llama_vocab_type(ctx) != LLAMA_VOCAB_TYPE_BPE) {
+        fprintf(stderr, "%s : error: vocab type is not SPM\n", __func__);
+        llama_free_model(model);
+        llama_free(ctx);
+        return 2;
+    }
+
+    bool success = true;
+
+    for (const auto & test_kv : k_tests()) {
+        const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, false);
+
+        printf("\n");
+        printf("src: '%s'\n", test_kv.first.c_str());
+        printf("res: '%s'\n", llama_detokenize_bpe(ctx, res).c_str());
+        printf("tok: ");
+        for (const auto & tok : res) {
+            printf("%d ", tok);
+        }
+        printf("\n");
+
+        bool correct = res.size() == test_kv.second.size();
+
+        for (int i = 0; i < (int) res.size() && correct; ++i) {
+            if (test_kv.second[i] != res[i]) {
+                correct = false;
+            }
+        }
+
+        if (!correct) {
+            fprintf(stderr, "%s : failed test:    '%s'\n", __func__, test_kv.first.c_str());
+            fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
+                llama_detokenize_bpe(ctx, res).c_str(),
+                llama_detokenize_bpe(ctx, test_kv.second).c_str());
+            fprintf(stderr, "%s : expected tokens: ", __func__);
+            for (const auto & t : test_kv.second) {
+                fprintf(stderr, "%6d, ", t);
+            }
+            fprintf(stderr, "\n");
+            fprintf(stderr, "%s : got tokens:      ", __func__);
+            for (const auto & t : res) {
+                fprintf(stderr, "%6d, ", t);
+            }
+            fprintf(stderr, "\n");
+
+            success = false;
+        }
+    }
+
+    if (!fname_text.empty()) {
+        fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str());
+
+        std::string text;
+        {
+            std::ifstream ifs(fname_text);
+            if (!ifs) {
+                fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_text.c_str());
+                return 1;
+            }
+            text = std::string(std::istreambuf_iterator<char>(ifs), std::istreambuf_iterator<char>());
+        }
+
+        fprintf(stderr, "%s : text size: %zu\n", __func__, text.size());
+
+        const std::vector<llama_token> res = llama_tokenize(ctx, text, true);
+
+        fprintf(stderr, "%s : tokens: %zu\n", __func__, res.size());
+
+        {
+            const std::string fname_out = fname_text + ".tokcpp";
+
+            std::ofstream ofs(fname_out);
+            if (!ofs) {
+                fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str());
+                return 1;
+            }
+
+            for (const auto & tok : res) {
+                ofs << tok << " ";
+            }
+
+            ofs << "\n";
+        }
+
+        fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str());
+    }
+
+    llama_free_model(model);
+    llama_free(ctx);
+
+    llama_backend_free();
+
+    return success ? 0 : 3;
+}
--- a/tests/test-tokenizer-0-falcon.py
+++ b/tests/test-tokenizer-0-falcon.py
@@ -0,0 +1,83 @@
+# tests with BPE tokenizer
+
+import os
+import sys
+import argparse
+
+from transformers import AutoTokenizer
+
+parser = argparse.ArgumentParser()
+parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file")
+parser.add_argument("--fname-tok",   help="path to a text file to tokenize")
+args = parser.parse_args()
+
+dir_tokenizer = args.dir_tokenizer
+
+tokenizer = AutoTokenizer.from_pretrained(dir_tokenizer)
+
+tests = [
+        "",
+        " ",
+        "  ",
+        "   ",
+        "\t",
+        "\n",
+        "\t\n",
+        "Hello world",
+        " Hello world",
+        "Hello World",
+        " Hello World",
+        " Hello World!",
+        "Hello, world!",
+        " Hello, world!",
+        " this is 🦙.cpp",
+        "w048 7tuijk dsdfhu",
+        "нещо на Български",
+        "កាន់តែពិសេសអាចខលចេញ",
+        "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
+        "Hello",
+        " Hello",
+        "  Hello",
+        "   Hello",
+        "    Hello",
+        "    Hello\n    Hello",
+    ]
+
+for text in tests:
+    print('text: ', text)
+    print(tokenizer.encode(text))
+    print(tokenizer.decode(tokenizer.encode(text)))
+
+print("\n\ntests for C++:\n")
+for text in tests:
+    res = tokenizer.encode(text)
+
+    k = text.replace('\n', '\\n')
+    k = k.replace('\t', '\\t')
+    k = '"' + k + '"'
+    print("{ %-24s, { " % k, end='')
+    for x in res:
+        print("%7d," % x, end='')
+    print(" }, },")
+
+print(tokenizer.encode('hello'))
+print(tokenizer.encode('world'))
+print(tokenizer.encode(' world'))
+print(tokenizer.encode('hello world'))
+
+fname_tok = args.fname_tok
+if fname_tok:
+    print('tokenizing file: ', fname_tok)
+    fname_out = fname_tok + '.tok'
+    with open(fname_tok, 'r') as f:
+        lines = f.readlines()
+        s = ''.join(lines)
+        res = tokenizer.encode(s)
+        # write to file
+        with open(fname_out, 'w') as f:
+            for x in res:
+                f.write(str(x) + ' ')
+            f.write('\n')
+        print('len(res): ', len(res))
+        print('len(lines): ', len(lines))
+    print('results written to: ', fname_out)
--- a/tests/test-tokenizer-0-llama.cpp
+++ b/tests/test-tokenizer-0-llama.cpp
@@ -0,0 +1,182 @@
+#include "llama.h"
+#include "common.h"
+
+#include <cstdio>
+#include <string>
+#include <map>
+#include <vector>
+#include <fstream>
+
+// generate using test-tokenizer-0-llama.py
+static const std::map<std::string, std::vector<llama_token>> & k_tests() {
+    static std::map<std::string, std::vector<llama_token>> _k_tests = {
+        { ""                      , {  }, },
+        { " "                     , {     259, }, },
+        { "  "                    , {    1678, }, },
+        { "   "                   , {     268, }, },
+        { "\t"                    , {   29871,     12, }, },
+        { "\n"                    , {   29871,     13, }, },
+        { "\t\n"                  , {   29871,     12,     13, }, },
+        { "Hello world"           , {   15043,   3186, }, },
+        { " Hello world"          , {   29871,  15043,   3186, }, },
+        { "Hello World"           , {   15043,   2787, }, },
+        { " Hello World"          , {   29871,  15043,   2787, }, },
+        { " Hello World!"         , {   29871,  15043,   2787,  29991, }, },
+        { "Hello, world!"         , {   15043,  29892,   3186,  29991, }, },
+        { " Hello, world!"        , {   29871,  15043,  29892,   3186,  29991, }, },
+        { " this is 🦙.cpp"        , {   29871,    445,    338,  29871,    243,    162,    169,    156,  29889,   8223, }, },
+        { "w048 7tuijk dsdfhu"    , {     281,  29900,  29946,  29947,  29871,  29955,   9161,  13535,  18031,   2176,   6905, }, },
+        { "нещо на Български"     , {    1538,   4851,    665,   1386,  29713,   1305, }, },
+        { "កាន់តែពិសេសអាចខលចេញ"   , {   29871,  31849,  31324,  31934,    228,    162,    142,    228,    161,    146,    228,    162,    133,    228,    161,    153,    228,    161,    186,  31708,    228,    162,    132,  31708,    228,    161,    165,  31324,    228,    161,    136,    228,    161,    132,    228,    161,    158,    228,    161,    136,    228,    162,    132,    228,    161,    140, }, },
+        { "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", {   29871,    243,    162,    157,    131,    313,   8945,  29897,  29871,    243,    162,    155,    185,  30722,    243,    162,    143,    174,  30598,    313,  20787,    953,   3848,    275,  16125,    630,  29897,  29871,  31681,    313,   6194,    953,  29877,   2397,    393,    756,    967,   1914,   5993,  29897, }, },
+        { "Hello"                 , {   15043, }, },
+        { " Hello"                , {   29871,  15043, }, },
+        { "  Hello"               , {     259,  15043, }, },
+        { "   Hello"              , {    1678,  15043, }, },
+        { "    Hello"             , {     268,  15043, }, },
+        { "    Hello\n    Hello"  , {     268,  15043,     13,   1678,  15043, }, },
+    };
+
+    return _k_tests;
+}
+
+int main(int argc, char **argv) {
+    if (argc < 2) {
+        fprintf(stderr, "Usage: %s vocab-file [text-file]\n", argv[0]);
+        return 1;
+    }
+
+    const std::string fname = argv[1];
+
+    std::string fname_text;
+    if (argc > 2) {
+        fname_text = argv[2];
+    }
+
+    fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
+
+    llama_model * model;
+    llama_context * ctx;
+
+    llama_backend_init(false);
+
+    // load the vocab
+    {
+        auto lparams = llama_context_default_params();
+
+        lparams.vocab_only = true;
+
+        model = llama_load_model_from_file(fname.c_str(), lparams);
+
+        if (model == NULL) {
+            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
+            return 1;
+        }
+
+        ctx = llama_new_context_with_model(model, lparams);
+
+        if (ctx == NULL) {
+            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
+            llama_free_model(model);
+            return 1;
+        }
+    }
+
+    if (llama_vocab_type(ctx) != LLAMA_VOCAB_TYPE_SPM) {
+        fprintf(stderr, "%s : error: vocab type is not SPM\n", __func__);
+        llama_free_model(model);
+        llama_free(ctx);
+        return 2;
+    }
+
+    bool success = true;
+
+    for (const auto & test_kv : k_tests()) {
+        const std::vector<llama_token> res_bos   = llama_tokenize(ctx, test_kv.first, true);
+        const std::vector<llama_token> res_nobos = llama_tokenize(ctx, test_kv.first, false);
+
+        printf("\n");
+        printf("src: '%s'\n", test_kv.first.c_str());
+        printf("res: '%s'\n", llama_detokenize_spm(ctx, res_bos).c_str());
+        printf("tok: ");
+        for (const auto & tok : res_bos) {
+            printf("%d ", tok);
+        }
+        printf("\n");
+
+        bool correct = res_nobos.size() == test_kv.second.size() && res_bos.size() == res_nobos.size() + 1 && res_bos[0] == 1;
+
+        for (int i = 0; i < (int) res_nobos.size() && correct; ++i) {
+            if (test_kv.second[i] != res_bos[i + 1]) {
+                correct = false;
+            }
+            if (test_kv.second[i] != res_nobos[i]) {
+                correct = false;
+            }
+        }
+
+        if (!correct) {
+            fprintf(stderr, "%s : failed test:    '%s'\n", __func__, test_kv.first.c_str());
+            fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
+                llama_detokenize_spm(ctx, res_nobos).c_str(),
+                llama_detokenize_spm(ctx, test_kv.second).c_str());
+            fprintf(stderr, "%s : expected tokens: ", __func__);
+            for (const auto & t : test_kv.second) {
+                fprintf(stderr, "%6d, ", t);
+            }
+            fprintf(stderr, "\n");
+            fprintf(stderr, "%s : got tokens:      ", __func__);
+            for (const auto & t : res_nobos) {
+                fprintf(stderr, "%6d, ", t);
+            }
+            fprintf(stderr, "\n");
+
+            success = false;
+        }
+    }
+
+    if (!fname_text.empty()) {
+        fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str());
+
+        std::string text;
+        {
+            std::ifstream ifs(fname_text);
+            if (!ifs) {
+                fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_text.c_str());
+                return 1;
+            }
+            text = std::string(std::istreambuf_iterator<char>(ifs), std::istreambuf_iterator<char>());
+        }
+
+        fprintf(stderr, "%s : text size: %zu\n", __func__, text.size());
+
+        const std::vector<llama_token> res = llama_tokenize(ctx, text, true);
+
+        fprintf(stderr, "%s : tokens: %zu\n", __func__, res.size());
+
+        {
+            const std::string fname_out = fname_text + ".tokcpp";
+
+            std::ofstream ofs(fname_out);
+            if (!ofs) {
+                fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str());
+                return 1;
+            }
+
+            for (const auto & tok : res) {
+                ofs << tok << " ";
+            }
+
+            ofs << "\n";
+        }
+
+        fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str());
+    }
+
+    llama_free_model(model);
+    llama_free(ctx);
+
+    llama_backend_free();
+
+    return success ? 0 : 3;
+}
--- a/tests/test-tokenizer-0-llama.py
+++ b/tests/test-tokenizer-0-llama.py
@@ -0,0 +1,95 @@
+# tests with SPM tokenizer
+
+import os
+import sys
+import argparse
+
+from sentencepiece import SentencePieceProcessor
+
+parser = argparse.ArgumentParser()
+parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file")
+parser.add_argument("--fname-tok",   help="path to a text file to tokenize")
+args = parser.parse_args()
+
+dir_tokenizer = args.dir_tokenizer
+
+tokenizer = SentencePieceProcessor(dir_tokenizer + '/tokenizer.model')
+
+tests = [
+        "",
+        " ",
+        "  ",
+        "   ",
+        "\t",
+        "\n",
+        "\t\n",
+        "Hello world",
+        " Hello world",
+        "Hello World",
+        " Hello World",
+        " Hello World!",
+        "Hello, world!",
+        " Hello, world!",
+        " this is 🦙.cpp",
+        "w048 7tuijk dsdfhu",
+        "нещо на Български",
+        "កាន់តែពិសេសអាចខលចេញ",
+        "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
+        "Hello",
+        " Hello",
+        "  Hello",
+        "   Hello",
+        "    Hello",
+        "    Hello\n    Hello",
+    ]
+
+
+for text in tests:
+    print('text: ', text)
+    print('\nwith bos:')
+    print(tokenizer.encode(text, add_bos=True))
+    print(tokenizer.decode(tokenizer.encode(text, add_bos=True)))
+    print('\nwithout bos:')
+    print(tokenizer.encode(text, add_bos=False))
+    print(tokenizer.decode(tokenizer.encode(text, add_bos=False)))
+
+print("'" + tokenizer.id_to_piece(15043) + "'") # '_Hello'
+print("'" + tokenizer.id_to_piece(29871) + "'") # '_'
+print("'" + tokenizer.decode([15043]) + "'")        # 'Hello'
+print("'" + tokenizer.decode([15043, 15043]) + "'") # 'Hello Hello'
+print("'" + tokenizer.decode([29871, 15043]) + "'")               # ' Hello'
+print("'" + tokenizer.decode([29871, 15043, 29871, 15043]) + "'") # ' Hello  Hello'
+
+print("\n\ntests for C++:\n")
+for text in tests:
+    res = tokenizer.encode(text, add_bos=False)
+
+    k = text.replace('\n', '\\n')
+    k = k.replace('\t', '\\t')
+    k = '"' + k + '"'
+    print("{ %-24s, { " % k, end='')
+    for x in res:
+        print("%7d," % x, end='')
+    print(" }, },")
+
+print(tokenizer.encode('hello'))
+print(tokenizer.encode('world'))
+print(tokenizer.encode(' world'))
+print(tokenizer.encode('hello world'))
+
+fname_tok = args.fname_tok
+if fname_tok:
+    print('tokenizing file: ', fname_tok)
+    fname_out = fname_tok + '.tok'
+    with open(fname_tok, 'r') as f:
+        lines = f.readlines()
+        s = ''.join(lines)
+        res = tokenizer.encode(s, add_bos=True)
+        # write to file
+        with open(fname_out, 'w') as f:
+            for x in res:
+                f.write(str(x) + ' ')
+            f.write('\n')
+        print('len(res): ', len(res))
+        print('len(lines): ', len(lines))
+    print('results written to: ', fname_out)
--- a/tests/test-tokenizer-0.cpp
+++ b/tests/test-tokenizer-0.cpp
@@ -1,140 +0,0 @@
-#include "llama.h"
-#include "common.h"
-
-#include <cstdio>
-#include <string>
-#include <map>
-#include <vector>
-
-static std::string unescape_whitespace(llama_context* ctx, const std::vector<llama_token>& tokens) {
-    std::string result;
-    for (size_t i = 0; i < tokens.size(); ++i) {
-        result += llama_token_to_str(ctx, tokens[i]);
-    }
-    return result;
-}
-
-static const std::map<std::string, std::vector<llama_token>> & k_tests() {
-    static std::map<std::string, std::vector<llama_token>> _k_tests = {
-        { " ",                      {1,    259, }, },
-        { "  ",                     { 1,    1678, }, },
-        { "   ",                    { 1,     268, }, },
-        { "\t",                     { 1,    29871,   12, }, },
-        { "\n",                     { 1,    29871,   13, }, },
-        { "\t\n",                   { 1,    29871,   12,     13, }, },
-        { "Hello world",            { 1,  15043,   3186, }, },
-        { " Hello world",           { 1,  29871,  15043,   3186, }, },
-        { "Hello World",            { 1,  15043,   2787, }, },
-        { " Hello World",           { 1,  29871,  15043,   2787, }, },
-        { " Hello World!",          { 1,  29871,  15043,   2787,  29991, }, },
-        { " this is 🦙.cpp",        { 1,  29871,    445,    338,  29871,    243,    162,    169,    156,  29889,   8223, }, },
-        { "w048 7tuijk dsdfhu",     { 1,    281,  29900,  29946,  29947,  29871,  29955,   9161,  13535,  18031,   2176,   6905, }, },
-        { "нещо на Български",      { 1,   1538,   4851,    665,   1386,  29713,   1305, }, },
-        { "កាន់តែពិសេសអាចខលចេញ",   { 1,  29871,  31849,  31324,  31934,    228,    162,    142,    228,    161,
-                                     146,    228,    162,    133,    228,    161,    153,    228,    161,    186,
-                                     31708,    228,    162,    132,  31708,    228,    161,    165,  31324,    228,
-                                     161,    136,    228,    161,    132,    228,    161,    158,    228,    161,
-                                     136,    228,    162,    132,    228,    161,    140, }, },
-        { "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
-            { 1,  29871,    243,    162,    157,    131,    313,   8945,  29897,  29871,
-                243,    162,    155,    185,  30722,    243,    162,    143,    174,  30598,
-                313,  20787,    953,   3848,    275,  16125,    630,  29897,  29871,  31681,
-                313,   6194,    953,  29877,   2397,    393,    756,    967,   1914,   5993,  29897, }, },
-        { "Hello",                  { 1,    15043 }, },
-        { " Hello",                 { 1,    29871,  15043 }, },
-        { "  Hello",                { 1,    259,    15043 }, },
-        { "   Hello",               { 1,    1678,   15043 }, },
-        { "    Hello",              { 1,    268,    15043 }, },
-        { "    Hello\n    Hello",   { 1,    268,    15043,  13,     1678,   15043 }, },
-    };
-
-    return _k_tests;
-}
-
-int main(int argc, char **argv) {
-    if (argc < 2) {
-        fprintf(stderr, "Usage: %s <vocab-file>\n", argv[0]);
-        return 1;
-    }
-
-    const std::string fname = argv[1];
-
-    fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
-
-    llama_model * model;
-    llama_context * ctx;
-
-    llama_backend_init(false);
-
-    // load the vocab
-    {
-        auto lparams = llama_context_default_params();
-
-        lparams.vocab_only = true;
-
-        model = llama_load_model_from_file(fname.c_str(), lparams);
-
-        if (model == NULL) {
-            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
-            return 1;
-        }
-
-        ctx = llama_new_context_with_model(model, lparams);
-
-        if (ctx == NULL) {
-            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
-            llama_free_model(model);
-            return 1;
-        }
-    }
-
-    const int n_vocab = llama_n_vocab(ctx);
-
-    if (n_vocab != 32000) {
-        fprintf(stderr, "%s : expected 32000 tokens, got %d\n", __func__, n_vocab);
-        llama_free_model(model);
-        llama_free(ctx);
-        return 2;
-    }
-
-    bool success = true;
-
-    for (const auto & test_kv : k_tests()) {
-        std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, true);
-        fprintf(stderr, "%s : '%s' tokenized to '%s'\n",
-            __func__, test_kv.first.c_str(), unescape_whitespace(ctx, res).c_str());
-
-        bool correct = res.size() == test_kv.second.size();
-
-        for (int i = 0; i < (int) res.size() && correct; ++i) {
-            if (res[i] != test_kv.second[i]) {
-                correct = false;
-            }
-        }
-
-        if (!correct) {
-            fprintf(stderr, "%s : failed test:    '%s'\n", __func__, test_kv.first.c_str());
-            fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
-                unescape_whitespace(ctx, res).c_str(), unescape_whitespace(ctx, test_kv.second).c_str());
-            fprintf(stderr, "%s : expected tokens: ", __func__);
-            for (const auto & t : test_kv.second) {
-                fprintf(stderr, "%6d, ", t);
-            }
-            fprintf(stderr, "\n");
-            fprintf(stderr, "%s : got tokens:      ", __func__);
-            for (const auto & t : res) {
-                fprintf(stderr, "%6d, ", t);
-            }
-            fprintf(stderr, "\n");
-
-            success = false;
-        }
-    }
-
-    llama_free_model(model);
-    llama_free(ctx);
-
-    llama_backend_free();
-
-    return success ? 0 : 3;
-}
--- a/tests/test-tokenizer-1.cpp
+++ b/tests/test-tokenizer-1.cpp
@@ -22,14 +22,6 @@ static std::string escape_whitespace(const std::string& text) {
    return result;
 }

-static std::string unescape_whitespace(llama_context * ctx, const std::vector<llama_token> & tokens) {
-    std::string result;
-    for (size_t i = 0; i < tokens.size(); ++i) {
-        result += llama_token_to_str(ctx, tokens[i]);
-    }
-    return result;
-}
-
 int main(int argc, char **argv) {
    if (argc < 2) {
        fprintf(stderr, "Usage: %s <vocab-file>\n", argv[0]);
@@ -72,13 +64,13 @@ int main(int argc, char **argv) {
    const int n_vocab = llama_n_vocab(ctx);

    for (int i = 0; i < n_vocab; ++i) {
-        std::string forward = llama_token_to_str(ctx, i);
+        std::string forward = llama_token_to_piece(ctx, i);
        std::vector<llama_token> tokens = llama_tokenize(ctx, forward, false);
        if (tokens.size() == 1) {
            if (i != tokens[0]) {
-                std::string backward = llama_token_to_str(ctx, tokens[0]);
+                std::string backward = llama_token_to_piece(ctx, tokens[0]);
                fprintf(stderr, "%s : error: token %d is string %s but bpe returns token %d %s\n",
-                    __func__, i, llama_token_to_str(ctx, i).c_str(), tokens[0], backward.c_str());
+                    __func__, i, llama_token_to_piece(ctx, i).c_str(), tokens[0], backward.c_str());
                return 2;
            }
        }
Author	SHA1	Message	Date
Georgi Gerganov	edd4c14817	llama : more tokenizer fixes (#2810 ) * tests : write a Python tokenizer test (wip) * llama : prefix input text for tokenization with whitespace * llama : distinguish pieces from decoded text + fix detokenization * common : add comments * examples : no longer manually add leading space when tokenizing * tests : use Python to generate tokenizer tests for C++ * tests : add option to tokenize text files ggml-ci * tests : add test-tokenizer-1.py * llama.cpp : fix LF token * hellaswag : move the concat space for clarity * tests : add falcon tests (py + cpp, currently do not pass Unicode) ggml-ci * common : temporary separate llama_detokenize calls for SPM and BPE --------- Co-authored-by: klosax <131523366+klosax@users.noreply.github.com>	2023-08-27 14:19:19 +03:00
Przemysław Pawełczyk	1591e2e590	ggml : detect SSSE3 (#2825 ) * ggml : add ggml_cpu_has_ssse3 * llama : show SSSE3 in system info	2023-08-27 11:10:25 +03:00
slaren	789c8c945a	ci : add LoRA test to CI (#2650 ) * ci : add lora test ggml-ci * move lora summary to the top, add lora logs ggml-ci * ci : decrease CPU ppl runs to 2 to avoide 20 min timeout ggml-ci * add 7b lora test use 1 thread for CUDA generation tests ggml-ci * add test with q8_0 (cpu only) ggml-ci --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	2023-08-27 10:03:27 +03:00
Bruce MacDonald	c1ac54b77a	server : add `/detokenize` endpoint (#2802 ) * Add a /detokenize endpoint to the example server * remove trailing white-space	2023-08-27 07:11:45 +08:00
Kerfuffle	730d9c681e	convert.py : advanced option (#2753 ) * Allow convert.py to convert to q8_0 Fix issue with bounded_parallel_map and greedy consuming iterator Display elapsed time during conversion * Add --concurrency option Minor improvements to help text Clean up bounded_parallel_map function a bit * Massive speed improvement thanks to Cebtenzzre * Refactor types	2023-08-26 23:13:36 +03:00
Tim Miller	c7d92e6dfe	llama : use Unicode Escape Sequence to replace encoded characters (#2814 ) The use of special characters within source files can break compiling on some computers with different region and language settings. Using Unicode escape sequences should allow for the code to be compiled on all setups without needing to change your computers settings or switch regions.	2023-08-26 21:27:07 +03:00
Tungsten842	61d1a2895e	flake.nix : add rocm support and cleanup (#2808 )	2023-08-26 21:19:44 +03:00
Cebtenzzre	741ca7dd1c	llama : move #includes out of _GNU_SOURCE conditional (#2817 )	2023-08-26 21:17:51 +03:00
Dr. Tom Murphy VII Ph.D	72f895c923	main : fix bug (penalize_nl=false doesn't work) + suppress warning on mingw (#1528 ) * Fix bug in main.cpp where penalize_nl=false has no effect. It modifies the underlying logits array, but at this point we are already working on the candidates copy. * Suppress redefinition warning for NOMINMAX on mingw. In my installation, this macro is already defined by /usr/lib/gcc/x86_64-w64-mingw32/11/include/c++/x86_64-w64-mingw32/bits/os_defines.h:45. * main : fix indentation * main : pass ctx to llama_token_nl() --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	2023-08-26 21:12:56 +03:00
Cebtenzzre	50526f37eb	llama : use std::abs in llama_sample_tail_free (#2800 ) Plain 'abs' casts the input to int.	2023-08-26 19:53:52 +03:00
Georgi Gerganov	04f4b1eb10	k-quants : remove unnecessary tensor shape restrictions (#2811 )	2023-08-26 17:37:35 +03:00
Kawrakow	7592375403	Better perplexity for 2- and 3-bit quantization for LLaMA-v2-70B (#2807 ) * Better perplexity for 2- and 3-bit quantization for the 70B model * PR comment --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>	2023-08-26 17:27:49 +03:00
Kawrakow	771551a793	Fix HellaSwag (#2805 ) Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>	2023-08-26 16:48:53 +03:00
Volodymyr Vitvitskyi	f305bad11e	flake : build llama.cpp on Intel with nix (#2795 ) Problem ------- `nix build` fails with missing `Accelerate.h`. Changes ------- - Fix build of the llama.cpp with nix for Intel: add the same SDK frameworks as for ARM - Add `quantize` app to the output of nix flake - Extend nix devShell with llama-python so we can use convertScript Testing ------- Testing the steps with nix: 1. `nix build` Get the model and then 2. `nix develop` and then `python convert.py models/llama-2-7b.ggmlv3.q4_0.bin` 3. `nix run llama.cpp#quantize -- open_llama_7b/ggml-model-f16.gguf ./models/ggml-model-q4_0.bin 2` 4. `nix run llama.cpp#llama -- -m models/ggml-model-q4_0.bin -p "What is nix?" -n 400 --temp 0.8 -e -t 8` Co-authored-by: Volodymyr Vitvitskyi <volodymyrvitvitskyi@SamsungPro.local>	2023-08-26 16:25:39 +03:00
Nigel Bosch	a2ca4e9de9	Handle null rope scaling value (#2793 )	2023-08-26 14:11:17 +02:00
klosax	2ba83c8685	Fix spm whitespaces (#2806 ) * llama.cpp : fix spm whitespace escaping + clean up * main.cpp : spm - add whitespace in front of prompt * test-tokenizer-0.cpp : spm - add whitespace in front of prompt	2023-08-26 13:45:53 +02:00
lon	bae5c5f679	examples : skip unnecessary external lib in server README.md how-to (#2804 )	2023-08-26 16:07:43 +08:00