Instructions to use win10/Qwopuagent-full-v6-35B-A3B-FP8-Block with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use win10/Qwopuagent-full-v6-35B-A3B-FP8-Block with Transformers:

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

pipe = pipeline("image-text-to-text", model="win10/Qwopuagent-full-v6-35B-A3B-FP8-Block")
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("win10/Qwopuagent-full-v6-35B-A3B-FP8-Block")
model = AutoModelForMultimodalLM.from_pretrained("win10/Qwopuagent-full-v6-35B-A3B-FP8-Block", 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 win10/Qwopuagent-full-v6-35B-A3B-FP8-Block with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "win10/Qwopuagent-full-v6-35B-A3B-FP8-Block"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "win10/Qwopuagent-full-v6-35B-A3B-FP8-Block",
		"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/win10/Qwopuagent-full-v6-35B-A3B-FP8-Block

SGLang

How to use win10/Qwopuagent-full-v6-35B-A3B-FP8-Block 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 "win10/Qwopuagent-full-v6-35B-A3B-FP8-Block" \
    --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": "win10/Qwopuagent-full-v6-35B-A3B-FP8-Block",
		"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 "win10/Qwopuagent-full-v6-35B-A3B-FP8-Block" \
        --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": "win10/Qwopuagent-full-v6-35B-A3B-FP8-Block",
		"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 win10/Qwopuagent-full-v6-35B-A3B-FP8-Block with Docker Model Runner:
```
docker model run hf.co/win10/Qwopuagent-full-v6-35B-A3B-FP8-Block
```

✨ win10/Qwopuagent-full-v6-35B-A3B-FP8-Block

A merged model created through a proprietary model-merging algorithm, delivering performance that surpasses the original base models.

🚀 Overview

This model was completed using a private model-merging algorithm. Its real-world performance is significantly stronger than the original models, and mathematically, it forms an almost perfect centroid.

In private agent usage, the experience is nothing short of exceptional. Aside from not yet surpassing GPT-5.5 in raw intelligence, there are virtually no major shortcomings.

The overall experience is already extremely close to GPT-5.5.

🧠 Research Highlight

This work demonstrates the successful merging of quantized models.

The resulting model quality exceeds that of the same model sources under BF16, showing that quantized-model merging can produce results beyond the original BF16-level model composition.

🙏 Credits

Special thanks to the authors of the following models:

Qwen/Qwen-AgentWorld-35B-A3B
infly/Infinity-Parser2-Pro
InternScience/Agents-A1
huihui-ai/Huihui-Nex-N2-mini-abliterated
Hcompany/Holo-3.1-35B-A3B
Gryphe/WorldSim-Opus-3.6-35B-A3B
AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16

💖 Open Sponsorship

Support this research and future model development:

Ko-fi
PayPal

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Safetensors

Model size

36B params

Tensor type

BF16

F8_E4M3

Model tree for win10/Qwopuagent-full-v6-35B-A3B-FP8-Block

Base model

nex-agi/Nex-N2-mini

Finetuned

huihui-ai/Huihui-Nex-N2-mini-abliterated

Quantized

(9)

this model