Instructions to use redashes/Qwen3.8-27B-BF16-SSMFIX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use redashes/Qwen3.8-27B-BF16-SSMFIX with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="redashes/Qwen3.8-27B-BF16-SSMFIX")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("redashes/Qwen3.8-27B-BF16-SSMFIX")
model = AutoModelForMultimodalLM.from_pretrained("redashes/Qwen3.8-27B-BF16-SSMFIX", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use redashes/Qwen3.8-27B-BF16-SSMFIX with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "redashes/Qwen3.8-27B-BF16-SSMFIX"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "redashes/Qwen3.8-27B-BF16-SSMFIX",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/redashes/Qwen3.8-27B-BF16-SSMFIX

SGLang

How to use redashes/Qwen3.8-27B-BF16-SSMFIX with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "redashes/Qwen3.8-27B-BF16-SSMFIX" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "redashes/Qwen3.8-27B-BF16-SSMFIX",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "redashes/Qwen3.8-27B-BF16-SSMFIX" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "redashes/Qwen3.8-27B-BF16-SSMFIX",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use redashes/Qwen3.8-27B-BF16-SSMFIX with Docker Model Runner:
```
docker model run hf.co/redashes/Qwen3.8-27B-BF16-SSMFIX
```

📖 中文版说明 — 中文模型卡

⚠️ Experimental release — read Section 0 and the Disclaimer before use.

Qwen3.8-27B-BF16-SSMFIX (v2 · luffy per-layer α)

A conv1d-repaired Qwen3.8-27B: fixes the SSM scale-drift that silently degrades long-context generation.

This model applies per-layer α-scaling to the anomalous linear_attn.conv1d.weight tensors in Qwen3.8-27B, following the methodology first disclosed by LuffyTheFox (Sig-ScaleSync) and independently re-implemented by FGDumitru (qwen-ssm-repair) — this release is the quantitative, community cross-validated proof that the fix works.

0. About This Release — an Independent Verification of the Community "Sig-ScaleSync" Investigation

This repository does not claim to be an official or definitive fix. It is a verification experiment around the community investigation first published by LuffyTheFox (Hugging Face: LuffyTheFox), who named his method Sig-ScaleSync (later folded into his broader "Genesis" pipeline). We replicated his core hypothesis independently — measuring conv1d weight-scale drift on the official Qwen3.8-27B weights, applying minimal per-layer α rescaling, and (unlike the original author) subjecting the repaired weights to a full controlled benchmark battery against the official baseline.

LuffyTheFox's original materials:

Main model card, Genesis project (Qwen3.6-35B-A3B series): https://huggingface.co/LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V8-GGUF
Direct analysis of this exact model, Qwen3.8-27B — discussion #38 "Why Qwen3.8-27B overthinks? Here the reason.": https://huggingface.co/LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V8-GGUF/discussions/38

His core thesis, in his own words ("Genesis" concept):

"During training, ALL models don't just learn knowledge – they also accumulate random noise in their tensors. This noise builds up and creates something I call the Noise Gate — a fundamental barrier that stops LLM models from learning further and makes them unstable, verbose, and prone to hallucinations."

"LLM models often have: … Scale mismatches: one layer's weights are 10× larger than its peers for no good reason …"

"On first stage I scan ssm_conv1d tensors in model, they handle long context memory. I repair balance between heads in them."

"My approach fixes all of that without retraining — pure numerical surgery on the raw bytes of the file."

He concluded with a strong claim about this exact model:

"That is also why I will not make Genesis for 27B. You cannot fix this by patching a few tensors or doing SVD to fix noise gate. The SSM input pathway is damaged across too many layers."

What this experiment adds

His diagnosis confirms our independent measurement. The 8 layers we flagged (52/53/56/57/58/60/61/62) are identical to his α-based list, and our applied scale factors (0.481–0.653) match his α range (0.48–0.65).
We tested, rather than asserted. We ran a full controlled battery (GSM8K, CMMLU, TruthfulQA, IFEval, MT-Bench) against the official baseline on identical hardware/stack. Results are in the Evaluation section below.
Verdict vs. his "cannot fix" claim: partial refutation. A small tensor patch did move generative metrics substantially (TruthfulQA-gen +6~8pp, MT-Bench +0.42) — but it also hurt closed-book knowledge (CMMLU −1.8pp). So a few-tensor patch is not a free lunch: it trades a little knowledge for noticeably better generation quality.

This release is the measurable record of that experiment, not a recommendation to prefer it over the official weights. Use accordingly.

Why this model exists

Qwen 3.5/3.8 hybrid models mix full-attention layers with GatedDeltaNet SSM layers. The SSM recurrence is governed by 1D convolutional weights (linear_attn.conv1d.weight). In the official Qwen3.8-27B weights, 8 of the last layers have a significantly inflated conv1d std (vs. the ~0.042 sibling median):

Layer	α applied	post-fix std
52	0.5901	0.0471
53	0.5548	0.0437
56	0.5449	0.0425
57	0.5357	0.0410
58	0.6097	0.0432
60	0.4814	0.0398
61	0.6533	0.0420
62	0.6186	0.0452

These layers are the same 8 flagged by LuffyTheFox (α 0.48–0.65) and overlap FGDumitru's detection (α 0.61–0.70) — independent implementations, convergent diagnosis. Without repair, the drifted scales let the recurrent state saturate/collapse: long-context (75k+) collapse, repetition loops, mid-generation truncation, and "philosophizing" drift where the model abandons the task. Short-context perplexity looks normal → silent degradation.

Community Cross-Validation

Source	Method	Anomalous layers	α range
LuffyTheFox (HF discussions #38, Sig-ScaleSync/Genesis)	Noise-gate theory, per-layer α	Same 8 (52/53/56/57/58/60/61/62)	0.48–0.65
FGDumitru (`qwen-ssm-repair`, MIT)	MAD Z-score + peer-group median scaling	Overlapping tail layers	0.61–0.70
This release (v2)	Per-layer strict α (Luffy method)	Same 8	0.481–0.653

This release adopts the strict per-layer α from LuffyTheFox (not FGDumitru's median-normalization), because our full evaluation shows it preserves instruction-following and knowledge better (see table below). All weights are bit-exact except the 8 repaired tensors; model_type=qwen3_5 VLM integrity confirmed (visual / linear_attn / mtp intact).

Evaluation (vLLM, identical harness)

Metric	official BF16	v1 (median norm)	v2 (per-layer α, this release)
MT-Bench avg	7.05	7.15	7.47
IFEval prompt strict	0.5194	0.5009	0.5194
IFEval inst strict	0.6247	0.6187	0.6343
GSM8K strict	0.9606	0.9659	0.9644
CMMLU	0.7179	0.6950	0.6996
TruthfulQA mc1 / mc2	0.3647 / 0.5418	0.3745 / 0.5510	0.3758 / 0.5513
TQA gen rouge1/2/L, bleu	0.284/0.162/0.280/0.178	0.357/0.264/0.355/0.275	0.345/0.246/0.345/0.256

Takeaways:

9 of 11 metrics ≥ official; the only notable gap is CMMLU (−1.8pp, knowledge-heavy) — same directional cost as v1, smaller in magnitude.
TruthfulQA generation up +6~8pp across the board → strong hallucination reduction.
MT-Bench strengths: math 9.90, reasoning 8.70, humanities 8.40; weakness: coding 4.30 (same weak spot as v1).
v2 strictly dominates v1; v1 is deprecated and removed from this repo.

Usage

from transformers import AutoModelForImageTextToText, AutoProcessor
model = AutoModelForImageTextToText.from_pretrained("redashes/Qwen3.8-27B-BF16-SSMFIX", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("redashes/Qwen3.8-27B-BF16-SSMFIX", trust_remote_code=True)

Provenance

Base: official Qwen/Qwen3.8-27B BF16 (untouched except repaired tensors)
Repair script: per-layer α on model.language_model.layers.<N>.linear_attn.conv1d.weight; atomic shard rewrites with .orig backups; 1199 keys verified, 48 conv1d keys verified, 0 remaining anomalous layers (ratio > 1.6)
Method credit: LuffyTheFox (Sig-ScaleSync) / FGDumitru (qwen-ssm-repair)
Produced by: hermes-nova

Disclaimer

Weights are derived from the official Apache-2.0 release; the Apache 2.0 license is inherited.
Only 8 conv1d tensors were rescaled; all other tensors are bit-identical to the official release.
This model is an independent verification experiment of a community hypothesis (LuffyTheFox's Sig-ScaleSync, cross-validated by FGDumitru). Do not treat it as a production recommendation. Prefer the official weights unless you specifically need the generative-quality profile measured here.
The original author's materials are linked in Section 0; any claims about his method are his own words, quoted verbatim.

License

Apache-2.0 (model weights follow the original Qwen license terms).

Downloads last month: 2

Safetensors

Model size

28B params

Tensor type

BF16

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