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Model: IntelLabs/sqft-mistral-7b-v0.3-50-base-gptq
Source: Original Platform

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README.md

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+---
+language: en
+license: apache-2.0
+library_name: transformers
+---
+
+# SQFT Base Model: sqft-mistral-7b-v0.3-50-base-gptq
+
+- Source Model: [mistralai/Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3)
+- Sparse Method: [Wanda](https://github.com/locuslab/wanda)
+- Sparsity: 50%
+- Quantization: GPTQ-INT4
+
+## Model Sources
+
+**Repository:** [https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/SQFT](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/SQFT)
+
+**Paper:**
+- [SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation Models](https://arxiv.org/abs/2410.03750)
+- [Low-Rank Adapters Meet Neural Architecture Search for LLM Compression](https://arxiv.org/abs/2501.16372)
+
+## How to get this model
+
+Refer to the commands in [SQFT/run_command/mistral-7b-v0.3/sparse_quantization.sh](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/SQFT/legacy/run_command/mistral-7b-v0.3/sparse_quantization.sh).
+
+## Citation
+
+```bash
+@inproceedings{munoz-etal-2024-sqft,
+    title = "{SQFT}: Low-cost Model Adaptation in Low-precision Sparse Foundation Models",
+    author = "Munoz, Juan Pablo  and
+      Yuan, Jinjie  and
+      Jain, Nilesh",
+    editor = "Al-Onaizan, Yaser  and
+      Bansal, Mohit  and
+      Chen, Yun-Nung",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
+    month = nov,
+    year = "2024",
+    address = "Miami, Florida, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.findings-emnlp.749",
+    pages = "12817--12832",
+}
+```
+
+## Acknowledgement
+
+Thanks to the sparse algorithm [Wanda]((https://arxiv.org/abs/2306.11695)) and the quantization method [GPTQ](https://arxiv.org/abs/2210.17323).
+
+## License
+
+Apache-2.0

config.json

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+{
+  "_name_or_path": "IntelLabs/sqft-mistral-7b-v0.3-50-base",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "quantization_config": {
+    "batch_size": 1,
+    "bits": 4,
+    "block_name_to_quantize": null,
+    "cache_block_outputs": true,
+    "damp_percent": 0.1,
+    "dataset": "c4",
+    "desc_act": false,
+    "exllama_config": {
+      "version": 1
+    },
+    "group_size": 128,
+    "max_input_length": null,
+    "model_seqlen": null,
+    "module_name_preceding_first_block": null,
+    "modules_in_block_to_quantize": null,
+    "pad_token_id": null,
+    "quant_method": "gptq",
+    "sym": true,
+    "tokenizer": null,
+    "true_sequential": true,
+    "use_cuda_fp16": false,
+    "use_exllama": false
+  },
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.39.1",
+  "use_cache": true,
+  "vocab_size": 32768
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.39.1"
+}

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special_tokens_map.json

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