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Xananthium
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Model Details
Model Provider
Xananthium
Model Tree
HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive
Input Modalities
Output Modalities
Supported Functionality
HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive,
using its highest-fidelity Q8_K_P GGUF artifact.Qwen/Qwen3.6-35B-A3B.auto_round:auto_gptq packing.The GGUF language tensors were dequantized, inverse Qwen3.6 layout transforms were applied, and the result was reconstructed into the native Hugging Face tensor layout. The official BF16 vision tower was then retained before the language checkpoint was packed to W4A16.
See reconstruction_metadata.json and
scripts/reconstruct_hauhau_qwen36.py for provenance and reconstruction
details.
The uploaded files are the same checkpoint used for the following local tests:
The tested deployment used two GPUs and a 180,000-token serving window:
bash
vllm serve Xananthium/Qwen3.6-35B-A3B-Hauhau-Aggressive-W4A16-Vision \--served-model-name qwen3.6-35b-hauhau-w4a16-vision \--tensor-parallel-size 2 \--dtype bfloat16 \--gpu-memory-utilization 0.94 \--max-model-len 180000 \--max-num-seqs 3 \--max-num-batched-tokens 32768 \--enable-prefix-caching \--enable-auto-tool-choice \--tool-call-parser qwen3_coder \--reasoning-parser qwen3 \--trust-remote-code
The local deployment also used 4-bit TurboQuant KV cache and 16 GiB native CPU KV offload. Those are runtime choices, not requirements of the checkpoint:
bash
--kv-cache-dtype turboquant_4bit_nc \--kv-offloading-backend native \--kv-offloading-size 16
For smaller GPUs, reduce --max-model-len, concurrency, or GPU memory
utilization. Runtime support for this packing format is required; the tested
stack was vLLM 0.27.1 with Transformers 5.15.1.
When served with the tested vLLM release, the model was validated through both OpenAI-compatible and Anthropic-compatible routes, including Anthropic image content blocks and native tool calls. This makes it suitable for Claude Code behind an authenticated reverse proxy.
This derivative is distributed under Apache-2.0, matching the source checkpoint. Credit belongs to the Qwen team for Qwen3.6 and HauhauCS for the aggressive language-model source. Review the source repositories for their full model cards and intended-use notes.