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README.md
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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 + QA-SparsePEFT
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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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MODEL_PATH=IntelLabs/sqft-qa-sparsepeft-phi-3-mini-4k-50-gptq-math-heu
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OUTPUT_DIR=./results
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python eval/evaluate_math.py --base_model_path ${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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