Huihui-Qwopus3.5-27B-v3-abliterated-NVFP4

Huihui-Qwopus3.5-27B-v3-abliterated-NVFP4

English | 繁體中文

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English

[!TIP] Quantized on 2026-04-13 with lm_head / linear_attn (GDN) / mtp / visual / embed_tokens preserved in BF16.

[!WARNING] NVIDIA DGX Spark (GB10 SM121) — Driver 590.48+ / CUDA 13.1+

As of April 2026, NVFP4 software support on SM121 is still incomplete. The native W4A4 compute path is not yet functional on this hardware — the runtime silently falls back to W4A16 (BF16 activations), negating the theoretical throughput advantage of FP4.

If accuracy and inference speed are your priority, we recommend the INT4 AutoRound version: 👉 YuYu1015/Huihui-Qwopus3.5-27B-v3-abliterated-int4-AutoRound

INT4 AutoRound leverages the mature W4A16 Marlin kernel path on DGX Spark, offering more thorough calibration (~99.5% quality retention) and significantly more stable performance. The full potential of NVFP4 will only be unlocked once NVIDIA delivers complete W4A4 kernel support for SM121.

NVFP4 quantization of huihui-ai/Huihui-Qwopus3.5-27B-v3-abliterated, optimized for NVIDIA DGX Spark (GB10 SM121).

Model Details

Quantization Details

Layers Preserved in BF16

The following layers are not quantized to preserve model quality:

Serving with vLLM

vllm serve /path/to/model \
    --quantization compressed-tensors \
    --served-model-name qwen3.5-27b \
    --reasoning-parser qwen3 \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_coder \
    --kv-cache-dtype auto \
    --gpu-memory-utilization 0.90 \
    --max-model-len 65536 \
    --enable-prefix-caching \
    --enable-chunked-prefill \
    --trust-remote-code \
    --language-model-only

DGX Spark (SM121) Compatibility Notes

• NVFP4 on SM121 falls back to W4A16 (native W4A4 path not yet supported, missing cvt.e2m1x2 instruction)
• FP8 KV cache is not compatible with GDN non-causal attention layers; use --kv-cache-dtype auto
• --language-model-only skips vision encoder profiling for text-only inference
• Clear page cache before starting on UMA: sudo sh -c 'echo 3 > /proc/sys/vm/drop_caches'

Safety Warning

This model has safety filtering removed (abliterated) and may generate inappropriate content. Users are solely responsible for all consequences arising from its use.

Credits

• Original Model: Qwen/Qwen3.5-27B by Alibaba Qwen Team
• Fine-tuning & Abliteration: huihui-ai
• NVFP4 Quantization: YuYu1015 on NVIDIA DGX Spark (GB10)
• Quantization Tool: llm-compressor by vLLM Project

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繁體中文

[!TIP] 2026-04-13 量化上傳，lm_head / linear_attn (GDN) / mtp / visual / embed_tokens 保留 BF16。

[!WARNING] NVIDIA DGX Spark (GB10 SM121) 使用者 — Driver 590.48+ / CUDA 13.1+

截至 2026 年 4 月，NVFP4 在 SM121 上的軟體支援仍不完整。原生 W4A4 運算路徑尚未在此硬體上就緒——執行時會靜默退回 W4A16（BF16 activation），FP4 的理論吞吐量優勢無法發揮。

若精度與推理速度為首要考量，建議改用 INT4 AutoRound 版本： 👉 YuYu1015/Huihui-Qwopus3.5-27B-v3-abliterated-int4-AutoRound

INT4 AutoRound 在 DGX Spark 上使用成熟的 W4A16 Marlin kernel 路徑，校準更完整（品質保留約 99.5%），效能顯著更穩定。待 NVIDIA 為 SM121 提供完整的 W4A4 kernel 支援後，NVFP4 的真正優勢才能發揮。

huihui-ai/Huihui-Qwopus3.5-27B-v3-abliterated 的 NVFP4 量化版本，針對 NVIDIA DGX Spark (GB10 SM121) 最佳化。

模型資訊

量化詳情

保留 BF16 的層

以下層未被量化以保持模型品質：

vLLM 部署

vllm serve /path/to/model \
    --quantization compressed-tensors \
    --served-model-name qwen3.5-27b \
    --reasoning-parser qwen3 \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_coder \
    --kv-cache-dtype auto \
    --gpu-memory-utilization 0.90 \
    --max-model-len 65536 \
    --enable-prefix-caching \
    --enable-chunked-prefill \
    --trust-remote-code \
    --language-model-only

DGX Spark (SM121) 相容性說明

• NVFP4 在 SM121 上會退回 W4A16（原生 W4A4 路徑尚未支援，缺少 cvt.e2m1x2 指令）
• FP8 KV cache 與 GDN non-causal attention 不相容，請使用 --kv-cache-dtype auto
• --language-model-only 跳過視覺編碼器 profiling，加速純文字推理啟動
• UMA 架構啟動前請先清除 page cache：sudo sh -c 'echo 3 > /proc/sys/vm/drop_caches'

安全警告

此模型已移除安全過濾機制（abliterated），可能產生不當內容。使用者須自行承擔所有風險與法律責任。

致謝

• 原始模型：Qwen/Qwen3.5-27B，Alibaba Qwen 團隊
• 微調與去審查：huihui-ai
• NVFP4 量化：YuYu1015，於 NVIDIA DGX Spark (GB10) 上完成
• 量化工具：llm-compressor，vLLM Project