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--- |
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language: en |
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license: apache-2.0 |
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--- |
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# SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation Models |
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- Base Model: [IntelLabs/sqft-phi-3-mini-4k-50-base-gptq](https://huggingface.co/IntelLabs/sqft-phi-3-mini-4k-50-base-gptq) |
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- Sparsity: 50% |
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- Quantization: INT4 (GPTQ) |
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- Finetune Method: SQFT |
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- Finetune data: 10K instruction-following math reasoning training dataset from [LLM-Adapters](https://github.com/AGI-Edgerunners/LLM-Adapters) ([math_10k.json](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/ft-training_set/math_10k.json)) |
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- Sub-Adapter: Heuristic |
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### Evaluation |
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```bash |
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git clone https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning.git haaml && cd haaml/SQFT |
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BASE_MODEL_PATH=IntelLabs/sqft-phi-3-mini-4k-50-base-gptq |
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ADAPTER_MODEL_PATH=IntelLabs/sqft-phi-3-mini-4k-50-gptq-math-heu-adapter |
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OUTPUT_DIR=./results |
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python eval/evaluate_math.py --base_model_path ${BASE_MODEL_PATH} --adapter_model_path ${ADAPTER_MODEL_PATH} --output_dir ${OUTPUT_DIR} |
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``` |
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Refer to our [repo](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/SQFT) for the environment information to run this command. |
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