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llama.cpp/examples/model-conversion/Makefile
Daniel Bevenius 72b44c0d21
model-conversion : merge inspect-org-model.py with tensor-info.py (#19823) 
This commit replaces/merges the inspect-org-model.py script with the
contents tensor-info.py script. The merged script has also been updated
to also print tensor sizes which was the only thing that was not done
before (by tensor-info.py that is).

The motivation for this is that tensor-info.py does not load the tensor
weights which can be time consuming for larger models. And also now that
both are doing almost the same thing it makes sense to just have one and
not two scripts to maintain.
2026-02-23 14:15:16 +01:00

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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="$(METADATA_OVERRIDE)" \
./scripts/causal/convert-model.sh
@MODEL_NAME="$(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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