Instructions to use skiboyvv/Qwopus3.6-27B-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use skiboyvv/Qwopus3.6-27B-v4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="skiboyvv/Qwopus3.6-27B-v4")
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("skiboyvv/Qwopus3.6-27B-v4")
model = AutoModelForMultimodalLM.from_pretrained("skiboyvv/Qwopus3.6-27B-v4")
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 skiboyvv/Qwopus3.6-27B-v4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "skiboyvv/Qwopus3.6-27B-v4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "skiboyvv/Qwopus3.6-27B-v4",
		"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/skiboyvv/Qwopus3.6-27B-v4

SGLang

How to use skiboyvv/Qwopus3.6-27B-v4 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 "skiboyvv/Qwopus3.6-27B-v4" \
    --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": "skiboyvv/Qwopus3.6-27B-v4",
		"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 "skiboyvv/Qwopus3.6-27B-v4" \
        --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": "skiboyvv/Qwopus3.6-27B-v4",
		"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 skiboyvv/Qwopus3.6-27B-v4 with Docker Model Runner:
```
docker model run hf.co/skiboyvv/Qwopus3.6-27B-v4
```

Qwopus3.6-27B-v4

A reasoning-enhanced version of Qwen3.6-27B, fine-tuned with structured Chain-of-Thought (CoT) distillation data to significantly improve mathematical reasoning and competition-level problem solving.

Model Description

Qwopus3.6-27B-v4 is obtained by supervised fine-tuning Qwen3.6-27B on a curated CoT reasoning corpus derived from Claude Opus extended thinking traces. The training follows a curriculum learning strategy — progressively scaling from short to long reasoning chains — to develop deep, structured reasoning without collapsing the model's instruction-following abilities.

The model produces explicit <think>...</think> reasoning traces before giving final answers, enabling transparent, step-by-step problem solving on competition mathematics and graduate-level science.

Benchmark Results

Benchmark	Base (Qwen3.6-27B)	Qwopus3.6-27B-v4	Δ
AIME 2025	56.67	86.67	+30.0 ✅
HMMT 2025	93.8	70	-23.8
GPQA Diamond	83.84	78.28	-5.56
MMLU-Pro (350)	88.3%	87.7%	-0.6%

AIME 2025: 86.67% — a +30 point improvement over the base model, demonstrating strong gains in competition-level mathematical reasoning through CoT distillation.

Note: Degradation on HMMT/GPQA/IFBench is attributed to distribution shift from the math-focused training corpus. General instruction-following and multi-domain reasoning are expected to improve in future versions with mixed-domain replay training.

Training Details

Parameter	Value
Base Model	Qwen3.6-27B
Method	LoRA SFT
LoRA Rank	32
LoRA Alpha	64
Learning Rate	2e-5
Epochs	2
Max Length	32,768
Training Framework	MS-Swift + DeepSpeed ZeRO-3
Hardware	8 × H200
Precision	bfloat16

Training Data

Trained on CoT-Trace-Inverted-28K — an openly released CoT distillation dataset constructed and curated by the author.

The dataset was built through a multi-stage pipeline:

CoT Collection: Extended thinking traces captured from Claude Opus 4.5/4.6/4.7 via API
Trace Inversion: Compressed reasoning bubbles reconstructed into full reasoning chains using Trace-Inverter-4B
Curriculum Sorting: Samples globally sorted by reasoning chain length for progressive training
Quality Filtering: NLP surface tasks, placeholder samples, and short-think samples removed

The dataset contains 28K high-quality CoT samples spanning mathematics, science, coding, and logical reasoning, and is publicly available at: 👉 https://huggingface.co/datasets/skiboyvv/CoT-Trace-Inverted-28K

Curriculum Learning Strategy

Training follows the three-stage curriculum from the original Qwopus research:

Stage	Context Length	Focus
Phase 1	≤ 4,096 tokens	Short reasoning, format establishment
Phase 2	4,096–8,192 tokens	Intermediate complexity, logic depth
Phase 3	8,192–32,768 tokens	Long-context reasoning + 10% short replay

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "skiboyvv/Qwopus3.6-27B-v4"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="bfloat16", device_map="auto")

messages = [
    {"role": "user", "content": "Find all integer bases b > 9 for which 17_b divides 97_b, and compute their sum."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=8192, temperature=0.6, top_p=0.95)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))

vLLM Deployment (Recommended)

vllm serve skiboyvv/Qwopus3.6-27B-v4 \
  --served-model-name qwopus-v4 \
  --tensor-parallel-size 4 \
  --max-model-len 32768 \
  --dtype bfloat16 \
  --enable-prefix-caching \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder

Limitations

Mathematical reasoning (AIME-style) is significantly enhanced, but general instruction-following degrades compared to the base model.
Not recommended as a drop-in replacement for general-purpose tasks.
Best suited for competition math, scientific reasoning, and structured problem solving.

Citation

@model{qwopus_v4_2025,
  author    = {Liu Jinhao},
  title     = {Qwopus3.6-27B-v4: CoT Distillation Fine-tuned Qwen3.6-27B},
  year      = {2025},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/skiboyvv/Qwopus3.6-27B-v4}
}

Model trained and released by Liu Jiahao. CoT distillation pipeline, curriculum design, and training configuration are original contributions of the author.

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