Create README.md

README.md

@@ -0,0 +1,240 @@
+---
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE
+language:
+- en
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-7B-Instruct
+tags:
+- chat
+- neuralmagic
+- llmcompressor
+---
+
+# Qwen2.5-7B-Instruct-quantized.w8a8
+
+## Model Overview
+- **Model Architecture:** Qwen2
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Activation quantization:** INT8
+  - **Weight quantization:** INT8
+- **Intended Use Cases:** Intended for commercial and research use multiple languages. Similarly to [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct), this models is intended for assistant-like chat.
+- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
+- **Release Date:** 10/09/2024
+- **Version:** 1.0
+- **Model Developers:** Neural Magic
+
+Quantized version of [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
+It achieves an average score of 58.80 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark version 1 and 35.60 on version 2, whereas the unquantized model achieves 57.50 on version 1 and 35.85 on version 2.
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights and activations of [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) to INT8 data type.
+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).
+Weight quantization also reduces disk size requirements by approximately 50%.
+
+Only weights and activations of the linear operators within transformers blocks are quantized.
+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.
+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.
+
+## Deployment
+
+This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
+
+```python
+from vllm import LLM, SamplingParams
+from transformers import AutoTokenizer
+
+model_id = "neuralmagic/Qwen2.5-7B-Instruct-quantized.w8a8"
+number_gpus = 1
+max_model_len = 8192
+
+sampling_params = SamplingParams(temperature=0.7, top_p=0.8, max_tokens=256)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+prompt = "Give me a short introduction to large language model."
+
+llm = LLM(model=model_id, tensor_parallel_size=number_gpus, max_model_len=max_model_len)
+
+outputs = llm.generate(prompt, sampling_params)
+
+generated_text = outputs[0].outputs[0].text
+print(generated_text)
+```
+
+vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
+
+
+## Evaluation
+
+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/387Bbd54bc621086e05aa1b030d8d4d5635b25e6) (commit 387Bbd54bc621086e05aa1b030d8d4d5635b25e6) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command:
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic/Qwen2.5-7B-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 \
+  --tasks openllm \
+  --batch_size auto
+```
+
+### Accuracy
+
+<table>
+  <tr>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong>Qwen2.5-7B-Instruct</strong>
+   </td>
+   <td><strong>Qwen2.5-7B-Instruct-quantized.w8a8 (this model)</strong>
+   </td>
+   <td><strong>Recovery</strong>
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="7" ><strong>OpenLLM v1</strong>
+   </td>
+   <td>MMLU (5-shot)
+   </td>
+   <td>66.27
+   </td>
+   <td>65.61
+   </td>
+   <td>99.0%
+   </td>
+  </tr>
+  <tr>
+   <td>ARC Challenge (25-shot)
+   </td>
+   <td>56.91
+   </td>
+   <td>57.25
+   </td>
+   <td>100.6%
+   </td>
+  </tr>
+  <tr>
+   <td>GSM-8K (5-shot, strict-match)
+   </td>
+   <td>17.29
+   </td>
+   <td>28.13
+   </td>
+   <td>162.7%
+   </td>
+  </tr>
+  <tr>
+   <td>Hellaswag (10-shot)
+   </td>
+   <td>75.19
+   </td>
+   <td>74.76
+   </td>
+   <td>99.4%
+   </td>
+  </tr>
+  <tr>
+   <td>Winogrande (5-shot)
+   </td>
+   <td>70.48
+   </td>
+   <td>69.30
+   </td>
+   <td>98.3%
+   </td>
+  </tr>
+  <tr>
+   <td>TruthfulQA (0-shot, mc2)
+   </td>
+   <td>58.84
+   </td>
+   <td>57.73
+   </td>
+   <td>101.0%
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>57.50</strong>
+   </td>
+   <td><strong>58.80</strong>
+   </td>
+   <td><strong>102.3%</strong>
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="7" ><strong>OpenLLM v2</strong>
+   </td>
+   <td>MMLU-Pro (5-shot)
+   </td>
+   <td>42.93
+   </td>
+   <td>42.40
+   </td>
+   <td>98.8%
+   </td>
+  </tr>
+  <tr>
+   <td>IFEval (0-shot)
+   </td>
+   <td>76.25
+   </td>
+   <td>75.30
+   </td>
+   <td>98.8%
+   </td>
+  </tr>
+  <tr>
+   <td>BBH (3-shot)
+   </td>
+   <td>55.56
+   </td>
+   <td>55.03
+   </td>
+   <td>99.1%
+   </td>
+  </tr>
+  <tr>
+   <td>Math-lvl-5 (4-shot)
+   </td>
+   <td>0.00
+   </td>
+   <td>0.00
+   </td>
+   <td>***
+   </td>
+  </tr>
+  <tr>
+   <td>GPQA (0-shot)
+   </td>
+   <td>33.07
+   </td>
+   <td>33.74
+   </td>
+   <td>102.3%
+   </td>
+  </tr>
+  <tr>
+   <td>MuSR (0-shot)
+   </td>
+   <td>40.60
+   </td>
+   <td>42.18
+   </td>
+   <td>103.9%
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>41.40</strong>
+   </td>
+   <td><strong>41.44</strong>
+   </td>
+   <td><strong>100.1%</strong>
+   </td>
+  </tr>
+</table>
+*** Reference value too low to report meaningful recovery.