Qwen3.8-LMHead

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What Qwen3.8-LMHead is

Model Overview Description: The RadixArk Qwen3.8-27B-NVFP4-BF16-LMHead model is a variant of RadixArk/Qwen3.8-27B-NVFP4, the quantized version of Qwen/Qwen3.8-27B produced at RadixArk using NVIDIA Model Optimizer with a mixed NVFP4 W4A4 recipe. The only difference from the source checkpoint is that lmhead is not quantized: it uses the original BF16 lmhead weights from Qwen/Qwen3.8-27B instead of the NVFP4 W4A4 quantized ones. All other tensors are identical to the source checkpoint. Run on SGLang: launch command and per-platform recipes in the Qwen3.8-27B cookbook. Third-Party Community Consideration This model is not owned or developed by RadixArk. It is a quantized derivative of Qwen's model; see the upstream Qwen3.8-27B model card for the source model's capabilities, training information, limitations, and license. License/Terms of Use: Apache License 2.0 Deployment Geography: Global <br Use Case: <br Developers looking to deploy an off-the-shelf, pre-quantized model in AI agent systems, chatbots, RAG systems, and other AI-powered applications. <br Release Date: <br Hugging Face 08/14/2026 via https://huggingface.co/RadixArk/Qwen3.8-27B-NVFP4 <br Model Architecture: Architecture Type: Transformers (Dense Multimodal) <br Network Architecture: Qwen3.8-27B <br Number of Model Parameters: 27B <br Input: Input Type(s): Text, image, and video <br Input Format(s): String and visual media <br Other Properties Related to Input: Native context length up to 262,144 tokens. <br Output: Output Type(s): Text <br Output Format: String <br Software Integration: Supported Runtime Engine(s): <br SGLang <br Supported Hardware Microarchitecture Compatibility: <br NVIDIA Blackwell (this checkpoint was produced and validated on GB300) <br Preferred Operating System(s): <br Linux <br Model Version(s): Quantized with NVIDIA Model Optimizer, commit 87c9f8cf83021957d1a1a575c90c9a4eaaf7ef0c. <br Training, Testing, and Evaluation Datasets: Calibration Data: Calibration used 1,024 samples from the abisee/cnndailymail training split with sequence length 512. <br Training Dataset: RadixArk did not train or fine-tune this checkpoint. Training information is inherited from the upstream Qwen3.

Summarised from the published model card. Read the full card on the HuggingFace links below.

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Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

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Call it like any OpenAI endpoint

Once AxForge deploys qwen3-8-lmhead for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (qwen3-8-lmhead below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen3-8-lmhead","messages":[{"role":"user","content":"Hello"}]}'

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