← back to catalog · registered 2026-09-17 03:56

jpezzulli/OrcaRouter-Qwen3.8-Flash-Next-Uncensored-ModelOpt-NVFP4

jpezzulli multimodal second-order
Abliteration classifier · v1.0.0
M-U
Primary method

Uncensored (method unknown)

No other method signals detected in this model.
Confidence
LOW
Why this label 3 signals
Weak or ambiguous signals. Best guess based on catalog patterns; treat as tentative and check the evidence below.
  • 'uncensored' in name/tags but no 'abliterated' marker
  • method not identifiable from author declaration alone
  • may be DPO fine-tune, prompt engineering, or unknown technique
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created 2026-09-17

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Metadata

License
other
Languages
en zh
Tags
transformers qwen qwen3.8 flash-next uncensored modelopt nvfp4 vision-language function-calling reasoning mtp image-text-to-text
Total size
0 B
Files
2
Quantizations
1
Registered
2026-09-17 03:56
Last updated on HF
2026-09-17 03:48

Files by quantization

Auxiliary files 2 files 4.85 KB
README.md 3.36 KB 63055ce2 download
.gitattributes 1.48 KB a6344aac download

README current version from Hugging Face


license: other
license_name: qwen-community-license-1.0
license_link: LICENSE
base_model:

  • OrcaRouter/Qwen3.8-Flash-Next-Uncensored
  • Qwen/Qwen3.8-Flash-Next
    base_model_relation: quantized
    pipeline_tag: image-text-to-text
    library_name: transformers
    language:
  • en
  • zh
    tags:
  • qwen
  • qwen3.8
  • flash-next
  • uncensored
  • modelopt
  • nvfp4
  • vision-language
  • function-calling
  • reasoning
  • mtp

OrcaRouter Qwen3.8 Flash-Next — ModelOpt NVFP4

Published by request from the Pennyroyal community. This is a ModelOpt NVFP4 conversion of OrcaRouter’s uncensored Qwen3.8 Flash-Next checkpoint—the conversion I use for local agentic work with Pennyroyal/SGLang.

I’ve also heard reports from several community members of it outperforming the stock model on agentic tasks, which added to the interest in sharing this working format.

The original model modification is OrcaRouter’s work. My contribution here is the quantization, compatible packaging and runtime testing—not additional fine-tuning or uncensoring.

Model lineage

Quantization and packaging

The checkpoint uses NVIDIA ModelOpt 0.46.0 NVFP4: 4-bit floating-point weights and activations, group size 16, on selected Linear modules. Attention and Gated-DeltaNet paths, routers, shared experts, hyper-connections, PLE, vision, embeddings, LM head and MTP are excluded from NVFP4 according to the packaged configuration. The large PLE embedding shards are FP8 E4M3. It is stored as sharded safetensors and is approximately 135.3 GB (126 GiB). Pennyroyal's optional online-FP8 optimizations are runtime settings, separate from this on-disk conversion. RAM versus NVMe PLE placement is also a runtime choice; see NVMe PLE setup.

This is the working checkpoint used for my daily agentic workload, including thinking and tool use, and tested with Pennyroyal’s Flash-Next native-MTP/FR-Spec path on an RTX PRO 6000 Blackwell. That is practical runtime evidence, not a claim of new benchmark scores or broad hardware compatibility.

Run with Pennyroyal

For a native installation, download the repository to a local directory and set that directory as TARGET_MODEL; then follow Launch Flash-Next with FR-Spec.

For the container, mount the parent model directory as HOST_MODELS_ROOT and set TARGET_MODEL to its corresponding path below /models, for example /models/OrcaRouter-Qwen3.8-Flash-Next-Uncensored-ModelOpt-NVFP4. See Choose a profile and configure .env.

The supplied source files have conflicting license metadata: the source README labels the model Apache-2.0, while its included LICENSE is Qwen Community License 1.0. This upload preserves that license file and declares the Hub license as other; review the included terms before use.

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