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Model: RedHatAI/Qwen2.5-0.5B-Instruct-quantized.w8a8 Source: Original Platform
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README.md
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---
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license: apache-2.0
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license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE
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language:
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- en
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pipeline_tag: text-generation
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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tags:
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- chat
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- neuralmagic
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- llmcompressor
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---
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# Qwen2.5-0.5B-Instruct-quantized.w8a8
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## Model Overview
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- **Model Architecture:** Qwen2
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- **Input:** Text
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- **Output:** Text
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- **Model Optimizations:**
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- **Activation quantization:** INT8
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- **Weight quantization:** INT8
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- **Intended Use Cases:** Intended for commercial and research use multiple languages. Similarly to [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct), this models is intended for assistant-like chat.
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- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
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- **Release Date:** 10/09/2024
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- **Version:** 1.0
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- **License(s):** [apache-2.0](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE)
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- **Model Developers:** Neural Magic
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Quantized version of [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct).
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It achieves an average score of 43.38 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark version 1 and 23.42 on version 2, whereas the unquantized model achieves 43.64 on version 1 and 23.39 on version 2.
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### Model Optimizations
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This model was obtained by quantizing the weights of [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) to INT8 data type.
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This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x).
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Weight quantization also reduces disk size requirements by approximately 50%.
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Only weights and activations of the linear operators within transformers blocks are quantized.
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Weights are quantized with a symmetric static per-channel scheme, where a fixed linear scaling factor is applied between INT8 and floating point representations for each output channel dimension.
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Activations are quantized with a symmetric dynamic per-token scheme, computing a linear scaling factor at runtime for each token between INT8 and floating point representations.
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## Deployment
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This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
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```python
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from vllm import LLM, SamplingParams
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from transformers import AutoTokenizer
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model_id = "neuralmagic/Qwen2.5-0.5B-Instruct-quantized.w8a8"
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number_gpus = 1
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max_model_len = 8192
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sampling_params = SamplingParams(temperature=0.7, top_p=0.8, max_tokens=256)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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prompt = "Give me a short introduction to large language model."
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llm = LLM(model=model_id, tensor_parallel_size=number_gpus, max_model_len=max_model_len)
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outputs = llm.generate(prompt, sampling_params)
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generated_text = outputs[0].outputs[0].text
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print(generated_text)
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```
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vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
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## Evaluation
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The model was evaluated on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) leaderboard tasks (version 1) with the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/383bbd54bc621086e05aa1b030d8d4d5635b25e6) (commit 383bbd54bc621086e05aa1b030d8d4d5635b25e6) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command:
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Qwen2.5-0.5B-Instruct-quantized.w8a8",dtype=auto,gpu_memory_utilization=0.9,add_bos_token=True,max_model_len=4096,enable_chunk_prefill=True,tensor_parallel_size=1 \
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--tasks openllm \
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--batch_size auto
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```
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### Accuracy
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#### Open LLM Leaderboard evaluation scores
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<table>
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<tr>
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<td><strong>Benchmark</strong>
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</td>
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<td><strong>Qwen2.5-0.5B-Instruct</strong>
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</td>
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<td><strong>Qwen2.5-0.5B-Instruct-quantized.w8a8 (this model)</strong>
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</td>
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<td><strong>Recovery</strong>
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</td>
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</tr>
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<tr>
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<td rowspan="7" ><strong>OpenLLM v1</strong>
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</td>
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<td>MMLU (5-shot)
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</td>
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<td>46.83
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</td>
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<td>46.29
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</td>
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<td>98.9%
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</td>
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</tr>
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<tr>
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<td>ARC Challenge (25-shot)
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</td>
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<td>33.62
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</td>
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<td>33.36
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</td>
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<td>99.2%
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</td>
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</tr>
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<tr>
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<td>GSM-8K (5-shot, strict-match)
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</td>
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<td>33.21
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</td>
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<td>33.21
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</td>
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<td>100.0%
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</td>
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</tr>
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<tr>
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<td>Hellaswag (10-shot)
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</td>
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<td>51.31
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</td>
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<td>50.97
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</td>
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<td>99.3%
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</td>
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</tr>
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<tr>
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<td>Winogrande (5-shot)
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</td>
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<td>55.01
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</td>
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<td>55.01
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</td>
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<td>100.0%
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</td>
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</tr>
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<tr>
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<td>TruthfulQA (0-shot, mc2)
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</td>
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<td>41.85
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</td>
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<td>41.47
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</td>
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<td>99.1%
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</td>
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</tr>
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<tr>
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<td><strong>Average</strong>
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</td>
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<td><strong>43.64</strong>
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</td>
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<td><strong>43.38</strong>
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</td>
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<td><strong>99.4%</strong>
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</td>
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</tr>
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<tr>
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<td rowspan="7" ><strong>OpenLLM v2</strong>
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</td>
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<td>MMLU-Pro (5-shot)
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</td>
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<td>17.49
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</td>
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<td>16.95
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</td>
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<td>96.9%
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</td>
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</tr>
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<tr>
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<td>IFEval (0-shot)
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</td>
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<td>31.17
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</td>
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<td>32.04
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</td>
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<td>102.8%
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</td>
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</tr>
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<tr>
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<td>BBH (3-shot)
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</td>
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<td>32.79
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</td>
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<td>32.51
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</td>
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<td>99.2%
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</td>
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</tr>
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<tr>
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<td>Math-lvl-5 (4-shot)
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</td>
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<td>0.21
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</td>
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<td>0.17
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</td>
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<td>***
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</td>
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</tr>
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<tr>
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<td>GPQA (0-shot)
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</td>
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<td>25.67
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</td>
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<td>26.12
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</td>
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<td>101.8%
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</td>
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</tr>
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<tr>
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<td>MuSR (0-shot)
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</td>
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<td>33.02
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</td>
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<td>32.75
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</td>
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<td>99.2%
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</td>
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</tr>
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<tr>
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<td><strong>Average</strong>
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</td>
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<td><strong>23.39</strong>
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</td>
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<td><strong>23.42</strong>
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</td>
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<td><strong>100.1%</strong>
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</td>
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</tr>
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</table>
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*** Reference value too low to report meaningful recovery.
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recipe.yaml
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quant_stage:
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quant_modifiers:
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GPTQModifier:
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sequential_update: true
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dampening_frac: 0.01
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ignore: [lm_head]
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scheme: W8A8
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targets: Linear
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observer: mse
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special_tokens_map.json
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BIN
vocab.json
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vocab.json
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