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llama.cpp/examples/model-conversion/Makefile
Daniel Bevenius 89730c8d26 model-conversion : add causal-convert-mmproj target [no ci] (#22969) 
* model-conversion : add causal-convert-mmproj target [no ci]

This commit adds a new Make target that only converts the mmproj model.

The motivation for this that the causal-convert-mm-model target will
convert both the test model and the mmproj model which is nice when the
model model conversion is finalized. But during development it is nice
to be able to just convert the mmproj model and not have to wait for
the often more time consuming text model conversion.

* add path model path validation check
2026-05-12 15:15:40 +02:00

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Makefile
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MAKEFLAGS += --no-print-directory
define validate_model_path
@if [ -z "$(MODEL_PATH)" ]; then \
echo "Error: MODEL_PATH must be provided either as:"; \
echo " 1. Environment variable: export MODEL_PATH=/path/to/model"; \
echo " 2. Command line argument: make $(1) MODEL_PATH=/path/to/model"; \
exit 1; \
fi
endef
define validate_embedding_model_path
@if [ -z "$(EMBEDDING_MODEL_PATH)" ]; then \
echo "Error: EMBEDDING_MODEL_PATH must be provided either as:"; \
echo " 1. Environment variable: export EMBEDDING_MODEL_PATH=/path/to/model"; \
echo " 2. Command line argument: make $(1) EMBEDDING_MODEL_PATH=/path/to/model"; \
exit 1; \
fi
endef
define quantize_model
@CONVERTED_MODEL="$(1)" QUANTIZED_TYPE="$(QUANTIZED_TYPE)" \
TOKEN_EMBD_TYPE="$(TOKEN_EMBD_TYPE)" OUTPUT_TYPE="$(OUTPUT_TYPE)" \
./scripts/utils/quantize.sh "$(1)" "$(QUANTIZED_TYPE)" "$(TOKEN_EMBD_TYPE)" "$(OUTPUT_TYPE)"
@echo "Export the quantized model path to $(2) variable in your environment"
endef
DEVICE ?= auto
###
### Casual Model targets/recipes
###
causal-convert-model-bf16: OUTTYPE=bf16
causal-convert-model-bf16: causal-convert-model
causal-convert-model-debug: DEBUG=--debug
causal-convert-model-debug: causal-convert-model
causal-convert-model:
$(call validate_model_path,causal-convert-model)
@MODEL_NAME="$(MODEL_NAME)" OUTTYPE="$(OUTTYPE)" MODEL_PATH="$(MODEL_PATH)" \
METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \
./scripts/causal/convert-model.sh $(DEBUG)
causal-convert-mm-model-bf16: OUTTYPE=bf16
causal-convert-mm-model-bf16: MM_OUTTYPE=f16
causal-convert-mm-model-bf16: causal-convert-mm-model
causal-convert-mm-model:
$(call validate_model_path,causal-convert-mm-model)
@MODEL_NAME="$(MODEL_NAME)" OUTTYPE="$(OUTTYPE)" MODEL_PATH="$(MODEL_PATH)" \
METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \
./scripts/causal/convert-model.sh
$(MAKE) causal-convert-mmproj MM_OUTTYPE="$(MM_OUTTYPE)"
causal-convert-mmproj:
$(call validate_model_path,causal-convert-mmproj)
@MODEL_NAME="$(MODEL_NAME)" OUTTYPE="$(MM_OUTTYPE)" MODEL_PATH="$(MODEL_PATH)" \
METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \
./scripts/causal/convert-model.sh --mmproj
causal-run-original-model:
$(call validate_model_path,causal-run-original-model)
@MODEL_PATH="$(MODEL_PATH)" ./scripts/causal/run-org-model.py --device "$(DEVICE)"
causal-run-converted-model:
@CONVERTED_MODEL="$(CONVERTED_MODEL)" ./scripts/causal/run-converted-model.sh
causal-verify-logits: causal-run-original-model causal-run-converted-model
@MODEL_PATH="$(MODEL_PATH)" ./scripts/causal/compare-logits.py
@MODEL_PATH="$(MODEL_PATH)" ./scripts/utils/check-nmse.py -m ${MODEL_PATH}
causal-run-original-embeddings:
@./scripts/causal/run-casual-gen-embeddings-org.py
causal-run-converted-embeddings:
@./scripts/causal/run-converted-model-embeddings-logits.sh
causal-verify-embeddings: causal-run-original-embeddings causal-run-converted-embeddings
@./scripts/causal/compare-embeddings-logits.sh
causal-inspect-original-model:
@./scripts/utils/inspect-org-model.py --list-all -s
causal-list-original-model-tensors:
@./scripts/utils/inspect-org-model.py --list-all-short -s
causal-inspect-converted-model:
@./scripts/utils/inspect-converted-model.sh
causal-start-embedding-server:
@./scripts/utils/run-embedding-server.sh ${CONVERTED_MODEL}
causal-curl-embedding-endpoint: causal-run-original-embeddings
@./scripts/utils/curl-embedding-server.sh | ./scripts/causal/compare-embeddings-logits.sh
causal-quantize-Q8_0: QUANTIZED_TYPE = Q8_0
causal-quantize-Q8_0: causal-quantize-model
causal-quantize-Q4_0: QUANTIZED_TYPE = Q4_0
causal-quantize-Q4_0: causal-quantize-model
# For Quantization Aware Trained (QAT) models in Q4_0 we explicitly set the
# token embedding and output types to Q8_0 instead of the default Q6_K.
causal-quantize-qat-Q4_0: QUANTIZED_TYPE = Q4_0
causal-quantize-qat-Q4_0: TOKEN_EMBD_TYPE = Q8_0
causal-quantize-qat-Q4_0: OUTPUT_TYPE = Q8_0
causal-quantize-qat-Q4_0: causal-quantize-model
causal-quantize-model:
$(call quantize_model,$(CONVERTED_MODEL),QUANTIZED_MODEL)
causal-run-quantized-model:
@QUANTIZED_MODEL="$(QUANTIZED_MODEL)" ./scripts/causal/run-converted-model.sh ${QUANTIZED_MODEL}
###
### Embedding Model targets/recipes
###
embedding-convert-model-bf16: OUTTYPE=bf16
embedding-convert-model-bf16: embedding-convert-model
embedding-convert-model:
$(call validate_embedding_model_path,embedding-convert-model)
@MODEL_NAME="$(MODEL_NAME)" OUTTYPE="$(OUTTYPE)" MODEL_PATH="$(EMBEDDING_MODEL_PATH)" \
METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \
./scripts/embedding/convert-model.sh
embedding-convert-model-st:
$(call validate_embedding_model_path,embedding-convert-model-st)
@MODEL_NAME="$(MODEL_NAME)" OUTTYPE="$(OUTTYPE)" MODEL_PATH="$(EMBEDDING_MODEL_PATH)" \
METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \
./scripts/embedding/convert-model.sh -st
embedding-run-original-model:
$(call validate_embedding_model_path,embedding-run-original-model)
@EMBEDDING_MODEL_PATH="$(EMBEDDING_MODEL_PATH)" \
USE_SENTENCE_TRANSFORMERS="$(USE_SENTENCE_TRANSFORMERS)" \
./scripts/embedding/run-original-model.py \
$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)") \
$(if $(USE_SENTENCE_TRANSFORMERS),--use-sentence-transformers)
embedding-run-original-model-st: USE_SENTENCE_TRANSFORMERS=1
embedding-run-original-model-st: embedding-run-original-model
embedding-run-converted-model:
@./scripts/embedding/run-converted-model.sh $(CONVERTED_EMBEDDING_MODEL) \
$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)") \
$(if $(EMBD_NORMALIZE),--embd-normalize "$(EMBD_NORMALIZE)")
embedding-verify-logits: embedding-run-original-model embedding-run-converted-model
@./scripts/embedding/compare-embeddings-logits.sh \
$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)")
embedding-verify-logits-st: embedding-run-original-model-st embedding-run-converted-model
@./scripts/embedding/compare-embeddings-logits.sh \
$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)")
embedding-inspect-original-model:
$(call validate_embedding_model_path,embedding-inspect-original-model)
@EMBEDDING_MODEL_PATH="$(EMBEDDING_MODEL_PATH)" ./scripts/utils/inspect-org-model.py -m ${EMBEDDING_MODEL_PATH} --list-all -s
embedding-inspect-converted-model:
@CONVERTED_EMBEDDING_MODEL="$(CONVERTED_EMBEDDING_MODEL)" ./scripts/utils/inspect-converted-model.sh ${CONVERTED_EMBEDDING_MODEL}
embedding-start-embedding-server:
@./scripts/utils/run-embedding-server.sh ${CONVERTED_EMBEDDING_MODEL}
embedding-curl-embedding-endpoint:
@./scripts/utils/curl-embedding-server.sh | ./scripts/embedding/compare-embeddings-logits.sh
embedding-quantize-Q8_0: QUANTIZED_TYPE = Q8_0
embedding-quantize-Q8_0: embedding-quantize-model
embedding-quantize-Q4_0: QUANTIZED_TYPE = Q4_0
embedding-quantize-Q4_0: embedding-quantize-model
# For Quantization Aware Trained (QAT) models in Q4_0 we explicitly set the
# token embedding and output types to Q8_0 instead of the default Q6_K.
embedding-quantize-qat-Q4_0: QUANTIZED_TYPE = Q4_0
embedding-quantize-qat-Q4_0: TOKEN_EMBD_TYPE = Q8_0
embedding-quantize-qat-Q4_0: OUTPUT_TYPE = Q8_0
embedding-quantize-qat-Q4_0: embedding-quantize-model
embedding-quantize-model:
$(call quantize_model,$(CONVERTED_EMBEDDING_MODEL),QUANTIZED_EMBEDDING_MODEL)
embedding-run-quantized-model:
@./scripts/embedding/run-converted-model.sh $(QUANTIZED_EMBEDDING_MODEL) \
$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)")
###
### Perplexity targets/recipes
###
perplexity-data-gen:
CONVERTED_MODEL="$(CONVERTED_MODEL)" ./scripts/utils/perplexity-gen.sh
perplexity-run-full:
QUANTIZED_MODEL="$(QUANTIZED_MODEL)" LOOGITS_FILE="$(LOGITS_FILE)" \
./scripts/utils/perplexity-run.sh
perplexity-run:
QUANTIZED_MODEL="$(QUANTIZED_MODEL)" ./scripts/utils/perplexity-run-simple.sh
###
### HuggingFace targets/recipes
###
hf-create-model:
@./scripts/utils/hf-create-model.py -m "${MODEL_NAME}" -ns "${NAMESPACE}" -b "${ORIGINAL_BASE_MODEL}"
hf-create-model-dry-run:
@./scripts/utils/hf-create-model.py -m "${MODEL_NAME}" -ns "${NAMESPACE}" -b "${ORIGINAL_BASE_MODEL}" -d
hf-create-model-embedding:
@./scripts/utils/hf-create-model.py -m "${MODEL_NAME}" -ns "${NAMESPACE}" -b "${ORIGINAL_BASE_MODEL}" -e
hf-create-model-embedding-dry-run:
@./scripts/utils/hf-create-model.py -m "${MODEL_NAME}" -ns "${NAMESPACE}" -b "${ORIGINAL_BASE_MODEL}" -e -d
hf-create-model-private:
@./scripts/utils/hf-create-model.py -m "${MODEL_NAME}" -ns "${NAMESPACE}" -b "${ORIGINAL_BASE_MODEL}" -p
hf-upload-gguf-to-model:
@./scripts/utils/hf-upload-gguf-model.py -m "${MODEL_PATH}" -r "${REPO_ID}" -o "${NAME_IN_REPO}"
hf-create-collection:
@./scripts/utils/hf-create-collection.py -n "${NAME}" -d "${DESCRIPTION}" -ns "${NAMESPACE}"
hf-add-model-to-collection:
@./scripts/utils/hf-add-model-to-collection.py -c "${COLLECTION}" -m "${MODEL}"
.PHONY: clean
clean:
@${RM} -rf data .converted_embedding_model.txt .converted_model.txt .embedding_model_name.txt .model_name.txt
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