Kimi-K3-INT4
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Kimi-K3-NVFP4
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View details ↗Kimi-K3-speculator.dspark
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View details ↗Laguna-S-2.1
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View details ↗Laguna-S-2.1-FP8
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View details ↗Laguna-S-2.1-NVFP4
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View details ↗Laguna-XS.2-FP8
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View details ↗Laguna-XS.2-NVFP4
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View details ↗Llama-3.1-8B-Instruct
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View details ↗Llama-3.1-8B-Instruct-NVFP4
RedHatAIThis model is a quantized version of Meta-Llama-3.1-8B-Instruct. It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model. This model was obtained by quantizing the weights and activations of Meta-Llama-3.1-8B-Instruct to FP4 data type, ready for inference with vLLM>=0.9.1 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 and activations of the linear operators within transformers blocks are quantized using LLM Compressor.
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View details ↗Llama-3.2-1B-FP8
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View details ↗Llama-3.2-1B-Instruct-FP8
RedHatAIQuantized version of Llama-3.2-1B-Instruct. It achieves scores within 1.0% of the scores of the unquantized model for MMLU, ARC-Challenge, GSM-8k, Hellaswag, Winogrande and TruthfulQA. This model was obtained by quantizing the weights of Llama-3.2-1B-Instruct 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 floating point representations for each output channel dimension. Activations are quantized with a symmetric per-tensor scheme, where a fixed linear scaling factor is applied between FP8 and floating point representations for the entire activation tensor. Weights are quantized by rounding to nearest FP8 representation. The llm-compressor library was applied to quantize the model, usin 512 sequences sequences taken from Neural Magic's LLM compression calibration dataset.
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View details ↗- **Model Architecture:** Meta-Llama-3.2 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Intended Use Cases:** Intended for commercial and research use in multiple languages. Similarly to Llama-3.2-1B-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:** 9/25/2024 - **Version:** 1.0 - **License(s):** llama3.2 - **Model Developers:** Neural Magic Quantized version of Llama-3.2-1B-Instruct. It achieves an average score of 50.88 on a subset of task from the OpenLLM benchmark (version 1), whereas the unquantized model achieves 51.70. This model was obtained by quantizing the weights and activations of Llama-3.2-1B-Instruct to FP8 data type, ready for inference with vLLM built from source. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
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View details ↗Quantized version of Llama-3.2-1B-Instruct. It achieves scores within 5% of the scores of the unquantized model for MMLU, ARC-Challenge, GSM-8k, Hellaswag, Winogrande and TruthfulQA. This model was obtained by quantizing the weights of Llama-3.2-1B-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. The SmoothQuant algorithm is used to alleviate outliers in the activations, whereas rhe GPTQ algorithm is applied for quantization. Both algorithms are implemented in the llm-compressor library. GPTQ used a 1% damping factor and 512 sequences sequences taken from Neural Magic's LLM compression calibration dataset.
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View details ↗Llama-3.2-1B-quantized.w8a8
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View details ↗Llama-3.2-3B-Instruct-FP8
RedHatAIQuantized version of Llama-3.2-3B-Instruct. It achieves scores within 0.7% of the scores of the unquantized model for MMLU, ARC-Challenge, GSM-8k, Hellaswag, Winogrande and TruthfulQA. This model was obtained by quantizing the weights of Llama-3.2-3B-Instruct 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 floating point representations for each output channel dimension. Activations are quantized with a symmetric per-tensor scheme, where a fixed linear scaling factor is applied between FP8 and floating point representations for the entire activation tensor. Weights are quantized by rounding to nearest FP8 representation. The llm-compressor library was applied to quantize the model, usin 512 sequences sequences taken from Neural Magic's LLM compression calibration dataset.
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View details ↗- **Model Architecture:** Meta-Llama-3.2 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Intended Use Cases:** Intended for commercial and research use in multiple languages. Similarly to Llama-3.2-3B-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:** 9/25/2024 - **Version:** 1.0 - **License(s):** llama3.2 - **Model Developers:** Neural Magic Quantized version of Llama-3.2-3B-Instruct. It achieves an average score of 50.88 on a subset of task from the OpenLLM benchmark (version 1), whereas the unquantized model achieves 51.70. This model was obtained by quantizing the weights and activations of Llama-3.2-3B-Instruct to FP8 data type, ready for inference with vLLM built from source. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
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View details ↗Llama-3.2-3B-quantized.w8a8
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View details ↗Llama-3.3-70B-Instruct
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View details ↗- **Model Architecture:** Meta-Llama-3.1 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Intended Use Cases:** Intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.3 model also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.3 Community License allows for these use cases. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.3 Community License. Use in languages beyond English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. - **Release Date:** 12/11/2024 - **Version:** 1.0 - **Validated on:** RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5 - **License(s):** llama3.3 - **Model Developers:** RedHat (Neural Magic) This model was obtained by quantizing activation and weights of Llama-3.3-70B-Instruct 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, whereas activations are quantized with a symmetric dynamic per-token scheme. The llm-compressor library is used for quantization.
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View details ↗Llama-3.3-70B-Instruct-NVFP4
RedHatAIThis model is a quantized version of Meta-Llama-3.3-70B-Instruct. It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model. This model was obtained by quantizing the weights and activations of Meta-Llama-3.3-70B-Instruct to FP4 data type, ready for inference with vLLM>=0.9.1 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 and activations of the linear operators within transformers blocks are quantized using LLM Compressor.
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View details ↗This model was obtained by quantizing the weights of Llama-3.3-70B-Instruct 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. This model was evaluated on the well-known OpenLLM v1, HumanEval, and HumanEval+ benchmarks. In all cases, model outputs were generated with the vLLM engine.
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