Create README.md

README.md

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+---
+tags:
+- vllm
+- sparsity
+pipeline_tag: text-generation
+license: llama3.1
+base_model: neuralmagic/Sparse-Llama-3.1-8B-ultrachat_200k-2of4
+datasets:
+- HuggingFaceH4/ultrachat_200k
+language:
+- en
+---
+
+# Sparse-Llama-3.1-8B-ultrachat_200k-2of4-FP8-dynamic
+
+## Model Overview
+- **Model Architecture:** Llama-3.1-8B
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Sparsity:** 2:4
+  - **Weight quantization:** FP8
+  - **Activation quantization:** FP8
+- **Release Date:** 11/15/2024
+- **Version:** 1.0
+- **License(s):** [llama3.1](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B/blob/main/LICENSE)
+- **Model Developers:** Neural Magic
+
+This is a multi-turn conversational 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 [ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) dataset, followed by quantization.
+On the [AlpacaEval](https://github.com/tatsu-lab/alpaca_eval) benchmark (version 1), it achieves a score of 62.9, compared to 62.0 for the fine-tuned dense model [Llama-3.1-8B-ultrachat_200k](https://huggingface.co/neuralmagic/Llama-3.1-8B-ultrachat_200k) — demonstrating a **99.4% accuracy recovery**.
+
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights of [Sparse-Llama-3.1-8B-ultrachat_200k-2of4](https://huggingface.co/neuralmagic/Sparse-Llama-3.1-8B-ultrachat_200k-2of4) to FP8 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 FP8 and BF16 representations for each output channel dimension.
+Linear scaling factors are computed via by minimizing the mean squarred error (MSE).
+Activations are quantized with a symmetric dynamic per-token scheme, computing a linear scaling factor at runtime for each token between FP8 and BF16 representations.
+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.
+
+
+## Deployment with vLLM
+
+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.
+
+
+## Evaluation
+
+This model was evaluated on Neural Magic's fork of [AlpacaEval](https://github.com/neuralmagic/alpaca_eval) benchmark.
+We adopt the same setup as in [Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment](https://arxiv.org/abs/2405.03594), using version 1 of the benchmark and [Llama-2-70b-chat](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) as the annotator.
+
+### Accuracy
+#### AlpacaEval Benchmark
+<table>
+    <tr>
+        <td><strong>Metric</strong></td>
+        <td style="text-align: center"><strong>Llama-3.1-8B-ultrachat_200k</strong></td>
+        <td style="text-align: center"><strong>Sparse-Llama-3.1-8B-ultrachat_200k-2of4</strong></td>
+        <td style="text-align: center"><strong>Sparse-Llama-3.1-8B-ultrachat_200k-2of4-FP8-dynamic</strong></td>
+    </tr>
+    <tr>
+        <td>Win rate</td>
+        <td style="text-align: center">62.0</td>
+        <td style="text-align: center">61.1</td>
+        <td style="text-align: center">62.9</td>
+    </tr>
+</table>