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batsclamp
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Model Details
Model Provider
batsclamp
Model Tree
huihui-ai/Huihui-Qwen3.8-27B-abliterated
Input Modalities
Output Modalities
Supported Functionality
| Weights | FP8 e4m3, per-channel (static) |
| Activations | FP8 e4m3, dynamic per-token |
| Format | compressed-tensors (float-quantized) |
| Calibration | none required (data-free pipeline) |
256 dense Linear modules are quantized. Everything the upstream
Qwen/Qwen3.8-27B-FP8 release leaves alone is left in BF16 here too:
linear_attn.* — the hybrid Mamba projections (in_proj_qkv, in_proj_a, in_proj_b, in_proj_z, out_proj)visual.blocks.*, visual.merger.*)embed_tokens, lm_head, and all normsmtp.*) — kept in BF16 rather than FP8, so speculative decoding still worksNote the upstream FP8 release uses per-tensor weight scales; this one uses per-channel, which is a slightly finer-grained (and therefore more accurate) scheme at the same size.
bash
vllm serve batsclamp/Huihui-Qwen3.8-27B-abliterated-FP8 \--max-model-len 262144 \--speculative-config '{"method": "mtp", "num_speculative_tokens": 3}' \--reasoning-parser qwen3 \--tool-call-parser qwen3_coder \--override-generation-config '{"temperature": 1.0, "top_p": 0.95, "top_k": 20}'
Sampling defaults follow the upstream model card's thinking-mode recommendation (temp 1.0 / top_p 0.95 / top_k 20).