Qwopus3.5-122B-A10B-Kimi-K2.6-destilled-abliterated-NVFP4

Overview

4-bit NVFP4 quantization of OpenYourMind/Qwopus3.5-122B-A10B-Kimi-K2.6-destill-healed-abliterated — the Kimi-K2.6-distilled, reasoning-DPO-healed, abliterated/uncensored evolution of Qwen/Qwen3.5-122B-A10B (Mixture of Experts, ~10B active / 122B total).

This build packs the transformer weights to NVFP4 with LLM Compressor, cutting the on-disk footprint from ~250 GB to ≈82 GB while keeping the vision tower, MTP head, router gates, and the Gated-DeltaNet attention path in higher precision. It is multimodal (image + text), uncensored, and — despite 4-bit weights — beats the full-precision Qwen3.5-122B-A10B baseline on every benchmark we ran (see Evaluation).

It loads anywhere compressed-tensors is supported and is auto-detected by vLLM (no --quantization flag needed).

Evaluation

Scores below were measured on this NVFP4 build and compared against the full-precision (BF16) Qwen/Qwen3.5-122B-A10B baseline:

Benchmark	Qwen3.5-122B-A10B (BF16, baseline)	Qwopus3.5 NVFP4 (this model)
CTI	64.8	71.5
LiveCodeBench	78.9	79.9
BFCL	72.2	85.6

Even after 4-bit (NVFP4) weight quantization, this model outperforms the BF16 Qwen3.5-122B-A10B baseline on all three benchmarks — the Kimi-K2.6 distillation + reasoning-DPO healing more than offsets any quantization loss. BFCL is the Berkeley Function-Calling Leaderboard (tool use); LiveCodeBench is contamination-controlled code generation.

Quantization (NVFP4)

Produced with LLM Compressor using the QuantizationModifier recipe shipped in this repo (recipe.yaml).

• Scheme: NVFP4 (format: nvfp4-pack-quantized) — 4-bit float weights in micro-blocks of 16, each block carrying an FP8 (float8_e4m3fn) scale. Weights are static; input activations are quantized dynamically (per-group, static-minmax).
• Quantized: all transformer Linear layers — attention projections and the 256 routed-expert MoE FFNs (37,056 packed weight tensors).
• Left in higher precision (BF16): the vision tower (visual.* — 333 tensors), the MTP head (model_mtp.safetensors — 785 tensors), lm_head, token embeddings, the MoE router gates (mlp.gate, shared_expert_gate), and the Gated-DeltaNet linear-attention path (linear_attn.*).
• Architecture preserved: Qwen3_5MoeForConditionalGeneration / model_type: qwen3_5_moe, so the checkpoint loads as a drop-in replacement for the base at the architecture level.

Downloads / Other Formats

Format	Repo	Use it for
Full BF16 weights	Qwopus3.5-122B-A10B-Kimi-K2.6-destill-healed-abliterated	Transformers / vLLM, fine-tuning, requantizing
NVFP4 (this repo)	Qwopus3.5-122B-A10B-Kimi-K2.6-destilled-abliterated-NVFP4	vLLM on a single ≥96 GB / Blackwell accelerator (vision + MTP included)
GGUF (Q4_K_M)	…-Kimi-K2.6-destill-healed-abliterated-GGUF	llama.cpp / LM Studio (text-only). MTP head included.
MLX 4-bit	…-Kimi-K2.6-destill-healed-abliterated-MLX-4bit	Apple Silicon / LM Studio (vision supported)

Files

File	Description	Size
model-00001-of-00002.safetensors	NVFP4-packed language weights (4-bit + FP8 scales) + lm_head	~50.0 GB
model-00002-of-00002.safetensors	NVFP4-packed language weights (tail) + BF16 vision tower	~26.4 GB
model_mtp.safetensors	BF16 MTP head (785 tensors, 1 hidden layer)	~5.0 GB
model.safetensors.index.json	Combined weight map	—
config.json	Multimodal config incl. quantization_config (nvfp4-pack-quantized)	—
recipe.yaml	LLM Compressor quantization recipe	—
tokenizer*, chat_template.jinja, generation_config.json, *preprocessor_config.json	Standard	—

Total on disk: ≈81.5 GB (~76 GiB).

Usage (vLLM)

vLLM auto-detects the NVFP4 compressed-tensors format — no --quantization flag.

vllm serve OpenYourMind/Qwopus3.5-122B-A10B-Kimi-K2.6-destilled-abliterated-NVFP4 \
  --tool-call-parser qwen3_coder \
  --reasoning-parser qwen3 \
  --max-model-len 262144

The checkpoint ships the MTP head, so you can enable 1-token speculative decoding:

  --speculative-config '{"num_speculative_tokens":1}'

Tip (Qwen3.5 MoE / Gated-DeltaNet): if torch.compile errors in the GDN path during startup, add --compilation-config '{"use_inductor_graph_partition":true}'.

Text + vision both work through AutoProcessor / AutoModelForImageTextToText (via the compressed-tensors integration) for non-vLLM workflows.

Vision & MTP

Both the vision tower and the MTP (multi-token-prediction) head are included and kept in BF16.

• Vision works as expected (image / video → text).
• MTP: the head is present and shape-compatible. It enables speculative decoding under vLLM, but on the upstream checkpoint it produced little measurable speedup/quality gain and would benefit from retraining — shipped intact for completeness and forward-compatibility.

Hardware

The NVFP4 weights are ≈82 GB (vs ~250 GB for the BF16 release), so the model runs on a single accelerator with ≥ 96 GB: H200, B200, RTX PRO 6000 Blackwell, or a 128 GB unified-memory NVIDIA DGX Spark / GB10. Native FP4 math requires a Blackwell GPU (compute capability ≥ 10.0 / sm_120+); on other hardware vLLM runs NVFP4 via FlashInfer/emulation.

Support & Community

• Discord: https://discord.gg/rhUZY5GEZr
• Bitcoin Donations: bc1qsvfduzj9fjs9fugpc52yver3f2g8fp7xjxecdv

Notes

• License: MIT (inherits from the upstream Qwen3.5 base license terms)
• Base Model: OpenYourMind/Qwopus3.5-122B-A10B-Kimi-K2.6-destill-healed-abliterated → Qwen/Qwen3.5-122B-A10B
• Quantization: NVFP4 (nvfp4-pack-quantized, group size 16) via LLM Compressor
• Modality: Text + Vision (image / video) + MTP
• Architecture: Qwen3 MoE (~10B active / 122B total) + Qwen3-VL vision tower + MTP head

Thanks

• Jackrong — for the idea of Qwopus merges (Opus distillations on Qwen models).
• wangzhang — for the wonderful abliterix framework, which was customized to do this abliteration.
• The LLM Compressor and vLLM teams for the NVFP4 tooling.

Disclaimer

Use is the responsibility of the user. Ensure your usage complies with applicable laws, platform rules, and deployment requirements.