169 lines
4.8 KiB
Markdown
169 lines
4.8 KiB
Markdown
---
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license: apache-2.0
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license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B/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
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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-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](https://huggingface.co/Qwen/Qwen2.5-0.5B), 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/blob/main/LICENSE)
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- **Model Developers:** Neural Magic
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Quantized version of [Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B).
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It achieves an average score of 43.93 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 44.03.
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### Model Optimizations
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This model was obtained by quantizing the weights of [Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) 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-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-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</strong>
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</td>
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<td><strong>Qwen2.5-0.5B-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>MMLU (5-shot)
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</td>
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<td>47.57
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</td>
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<td>47.35
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</td>
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<td>99.5%
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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>34.90
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</td>
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<td>34.47
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</td>
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<td>98.8%
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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>34.19
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</td>
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<td>34.19
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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>51.83
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</td>
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<td>51.63
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</td>
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<td>99.6%
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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>55.80
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</td>
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<td>55.64
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</td>
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<td>99.7%
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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>39.90
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</td>
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<td>40.32
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</td>
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<td>101.1%
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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>44.03</strong>
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</td>
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<td><strong>43.93</strong>
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</td>
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<td><strong>99.8%</strong>
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</td>
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</tr>
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</table>
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