This model is an updated quantized version of Qwen/Qwen3.8-27B, using a mixed-precision FP4/FP8 scheme with an unquantized language-model head and updated quantization scales. See Evaluation for accuracy results. This model was produced by applying mixed-precision quantization to Qwen/Qwen3.8-27B. MLP projections are quantized to FP4, attention projections and the final MLP layers are quantized to FP8, and the KV cache is quantized to FP8, while the language-model head is kept in full precision to preserve output quality. The quantization scales were updated by calibrating on a 512-sample subset of the perfectblend dataset with a recipe that combines AWQ and GPTQ. Only the weights and activations of the linear operators within the transformer blocks are quantized using LLM Compressor. The checkpoint is ~24.7 GB on disk (versus ~54 GB in BF16), reducing disk size and GPU memory requirements by roughly 70%.
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View details ↗This model was obtained by quantizing activations and weights of Qwen3-8B 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. The model was evaluated on the OpenLLM leaderboard tasks (versions 1 and 2), using lm-evaluation-harness, and on reasoning tasks using lighteval. vLLM was used for all evaluations.
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View details ↗This model was obtained by quantizing the weights of Qwen3-8B 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 asymmetric per-group scheme, with group size 64. The GPTQ algorithm is applied for quantization, as implemented in the llm-compressor library. The model was evaluated on the OpenLLM leaderboard tasks (versions 1 and 2), using lm-evaluation-harness, and on reasoning tasks using lighteval. vLLM was used for all evaluations.
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View details ↗- **Verifier:** Qwen/Qwen3-8B - **Speculative Decoding Algorithm:** EAGLE-3 - **Model Architecture:** Eagle3Speculator - **Release Date:** 07/27/2025 - **Version:** 1.0 - **Model Developers:** RedHat This is a speculator model designed for use with Qwen/Qwen3-8B, based on the EAGLE-3 speculative decoding algorithm. It was trained using the speculators library on a combination of the Aeala/ShareGPT_Vicuna_unfiltered and the HuggingFaceH4/ultrachat_200k datasets. The model was trained with thinking turned disabled. This model should be used with the Qwen/Qwen3-8B chat template, specifically through the `/chat/completions` endpoint. Use Case k=1 k=2 k=3 k=4 k=5 k=6 k=7
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View details ↗This model was obtained by quantizing the weights and activations of Qwen/Qwen3-Coder-Next to FP4 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 and activations of the linear operators within transformers blocks of the language model are quantized. The model was evaluated on the OpenLLM leaderboard task, using lm-evaluation-harness. vLLM was used for all evaluations.
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View details ↗This model is a quantized version of Qwen/Qwen3-VL-235B-A22B-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 Qwen/Qwen3-VL-235B-A22B-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 and activations of Qwen/Qwen3-VL-32B-Instruct to FP8 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 and activations of the linear operators within transformers blocks of the language model are quantized. The model was evaluated on the OpenLLMv1 leaderboard task, using lm-evaluation-harness. vLLM was used for all evaluations.
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View details ↗This repo contains model files for TinyLlama-1.1B-Chat-v1.0 optimized for NM-vLLM, a high-throughput serving engine for compressed LLMs. This model was pruned with SparseGPT, using SparseML. Install NM-vLLM for fast inference and low memory-usage:
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