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It achieves an average score of 88.98 on the HumanEval+ benchmark, whereas the unquantized model achieves 87.63. This model was obtained by quantizing the weights and activations of DeepSeek-Coder-V2-Instruct to FP8 data type, ready for inference with vLLM >= 0.5.2. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. In particular, this model can now be loaded and evaluated with only 4xH100 GPUs, as opposed to 8. Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-tensor quantization is applied, in which a single linear scaling maps the FP8 representations of the quantized weights and activations. AutoFP8 is used for quantization with 512 sequences of UltraChat.

Text generation
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- **Model Architecture:** DeepSeek-Coder-V2-Lite-Instruct - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Intended Use Cases:** Intended for commercial and research use in English. Similarly to Meta-Llama-3-7B-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). Use in languages other than English. - **Release Date:** 7/18/2024 - **Version:** 1.0 - **License(s):** deepseek-license - **Model Developers:** Neural Magic Quantized version of DeepSeek-Coder-V2-Lite-Instruct. It achieves an average score of 79.60 on the HumanEval+ benchmark, whereas the unquantized model achieves 79.33.

Text generation
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This model was obtained by quantizing the weights of DeepSeek-R1-Distill-Qwen-32B to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. Only the weights of the linear operators within transformers blocks are quantized. Weights are quantized using a symmetric per-group scheme, with group size 128. The GPTQ algorithm is applied for quantization, as implemented in the llm-compressor library. Category Metric deepseek-ai/DeepSeek-R1-Distill-Qwen-32B neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16 Recovery

Text generation
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- **Model Architecture:** DeepSeek-V2.5-1210 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Release Date:** 3/1/2025 - **Version:** 1.0 - **Model Developers:** Neural Magic Quantized version of DeepSeek-V2.5-1210. It achieves an average score of 77.8 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 77.82. This model was obtained by quantizing the weights and activations to FP8 data type, ready for inference with vLLM >= 0.5.2. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. The weights and activations of the linear operators within transformers blocks are quantized, except the MLP routers.

Text generation
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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This model is an FP8 quantized version of google/diffusiongemma-26B-A4B-it. The model has both weights and activations quantized to FP8 using vllm/llm-compressor and in the compressed-tensors format. It was evaluated on several tasks to assess its quality in comparison to the unquantized model using vLLM. The following metrics were generated when serving the quantized model with vLLM on a single B200 GPU.

Visual QA
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This model is an NVFP4 quantized version of google/diffusiongemma-26B-A4B-it. The model has both weights and activations quantized to NVFP4 using vllm/llm-compressor and in the compressed-tensors format. It was evaluated on several tasks to assess its quality in comparison to the unquantized model using vLLM. The following metrics were generated when serving the quantized model with vLLM on a single B200 GPU.

Visual QA
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