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--- |
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tags: |
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- vllm |
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- sparsity |
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- quantization |
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- int4 |
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pipeline_tag: text-generation |
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license: llama3.1 |
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base_model: neuralmagic/Sparse-Llama-3.1-8B-evolcodealpaca-2of4 |
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datasets: |
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- theblackcat102/evol-codealpaca-v1 |
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language: |
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- en |
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--- |
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# Sparse-Llama-3.1-8B-evolcodealpaca-2of4 |
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## Model Overview |
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- **Model Architecture:** Llama-3.1-8B |
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- **Input:** Text |
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- **Output:** Text |
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- **Model Optimizations:** |
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- **Sparsity:** 2:4 |
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- **Release Date:** 11/21/2024 |
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- **Version:** 1.0 |
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- **License(s):** [llama3.1](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B/blob/main/LICENSE) |
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- **Model Developers:** Neural Magic |
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This is a code completion AI model obtained by fine-tuning the 2:4 sparse [Sparse-Llama-3.1-8B-2of4](https://huggingface.co/neuralmagic/Sparse-Llama-3.1-8B-2of4) on the [evol-codealpaca-v1](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1) dataset, followed by quantization |
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On the [HumanEval](https://arxiv.org/abs/2107.03374) benchmark, it achieves a pass@1 of 50.6, compared to 48.5 for the fine-tuned dense model [Llama-3.1-8B-evolcodealpaca](https://huggingface.co/neuralmagic/Llama-3.1-8B-evolcodealpaca) — demonstrating over **100% accuracy recovery**. |
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### Model Optimizations |
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This model was obtained by quantizing the weights of [Sparse-Llama-3.1-8B-evolcodealpaca-2of4](https://huggingface.co/neuralmagic/Sparse-Llama-3.1-8B-evolcodealpaca-2of4) to INT4 data type. |
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This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. |
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That is on top of the reduction of 50% of weights via 2:4 pruning employed on [Sparse-Llama-3.1-8B-evolcodealpaca-2of4](https://huggingface.co/neuralmagic/Sparse-Llama-3.1-8B-evolcodealpaca-2of4). |
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Only the weights of the linear operators within transformers blocks are quantized. Symmetric per-channel quantization is applied, in which a linear scaling per output dimension maps the INT4 and floating point representations of the quantized weights. |
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The [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library. |
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## Deployment with vLLM |
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This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend. 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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This model was evaluated on Neural Magic's fork of [EvalPlus](https://github.com/neuralmagic/evalplus). |
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### Accuracy |
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#### Human Benchmark |
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<table> |
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<tr> |
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<td><strong>Metric</strong></td> |
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<td style="text-align: center"><strong>Llama-3.1-8B-evolcodealpaca</strong></td> |
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<td style="text-align: center"><strong>Sparse-Llama-3.1-8B-evolcodealpaca-2of4</strong></td> |
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<td style="text-align: center"><strong>Sparse-Llama-3.1-8B-evolcodealpaca-2of4-quantized.w4a16</strong></td> |
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</tr> |
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<tr> |
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<td>HumanEval pass@1</td> |
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<td style="text-align: center">48.5</td> |
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<td style="text-align: center">49.1</td> |
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<td style="text-align: center">50.6</td> |
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</tr> |
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<tr> |
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<td>HumanEval+ pass@1</td> |
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<td style="text-align: center">44.2</td> |
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<td style="text-align: center">46.3</td> |
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<td style="text-align: center">48.0</td> |
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</tr> |
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</table> |
