Instructions to use randomsubmit/GraphForge-Qwen3.6-27B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use randomsubmit/GraphForge-Qwen3.6-27B-SFT with Transformers:

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

pipe = pipeline("image-text-to-text", model="randomsubmit/GraphForge-Qwen3.6-27B-SFT")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("randomsubmit/GraphForge-Qwen3.6-27B-SFT")
model = AutoModelForMultimodalLM.from_pretrained("randomsubmit/GraphForge-Qwen3.6-27B-SFT", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use randomsubmit/GraphForge-Qwen3.6-27B-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "randomsubmit/GraphForge-Qwen3.6-27B-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "randomsubmit/GraphForge-Qwen3.6-27B-SFT",
		"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/randomsubmit/GraphForge-Qwen3.6-27B-SFT

SGLang

How to use randomsubmit/GraphForge-Qwen3.6-27B-SFT 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 "randomsubmit/GraphForge-Qwen3.6-27B-SFT" \
    --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": "randomsubmit/GraphForge-Qwen3.6-27B-SFT",
		"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 "randomsubmit/GraphForge-Qwen3.6-27B-SFT" \
        --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": "randomsubmit/GraphForge-Qwen3.6-27B-SFT",
		"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 randomsubmit/GraphForge-Qwen3.6-27B-SFT with Docker Model Runner:
```
docker model run hf.co/randomsubmit/GraphForge-Qwen3.6-27B-SFT
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GraphForge-Qwen3.6-27B-SFT

This repository contains a final GraphForge supervised fine-tuning checkpoint for working-agent tasks. It is an SFT model, not an RFT checkpoint.

The model was fine-tuned on 2,169 GraphForge trajectory examples for three epochs. The release includes model weights, inference configuration, and tokenizer and processor assets. It does not include training logs or training data.

Use the included chat template and a runtime supporting the model architecture. This is a research model. Outputs may be incorrect and should be checked before use.

This derivative is based on Qwen/Qwen3.6-27B. The upstream Apache 2.0 license is retained in LICENSE. Model weights were fine-tuned; release documentation was replaced and local provenance fields, where present, were removed for anonymous review.