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

Libraries

How to use groundhogLLM/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="groundhogLLM/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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("groundhogLLM/GraphForge-Qwen3.6-27B-SFT")
model = AutoModelForMultimodalLM.from_pretrained("groundhogLLM/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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use groundhogLLM/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 "groundhogLLM/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": "groundhogLLM/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/groundhogLLM/GraphForge-Qwen3.6-27B-SFT

SGLang

How to use groundhogLLM/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 "groundhogLLM/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": "groundhogLLM/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 "groundhogLLM/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": "groundhogLLM/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 groundhogLLM/GraphForge-Qwen3.6-27B-SFT with Docker Model Runner:
```
docker model run hf.co/groundhogLLM/GraphForge-Qwen3.6-27B-SFT
```

GraphForge-Qwen3.6-27B-SFT

This is a final GraphForge SFT checkpoint for file-based working agents, fine-tuned from Qwen/Qwen3.6-27B. This is not an RFT model.

Training overview

Item	Setting
Training corpus	GraphForge-SFT-2169
Training examples	2,169 trajectory sequences
Epochs	3
Data admission	Teacher agent-judge score strictly above 0.90
Reasoning history	Preserved in the training trajectories
Sequence-length policy	Discard complete samples above 262,144 tokens rather than truncate

The GraphForge 27B and 35B-A3B SFT releases use the same main training corpus. The data pipeline constructs tasks and evidence-anchored rubrics from real files, uses an initial execution and one revision stage, and exports admitted teacher trajectories for supervised learning. The teacher model is GLM-5.2.

Contents and inference

The repository contains the final safetensors weights, the weight index, model configuration, tokenizer, chat template, and available processor assets. It excludes optimizer state, training logs, and intermediate checkpoints. Use the included chat template with a compatible Transformers or serving runtime. The exported architecture identifier may use Qwen3.5-compatible implementation names; do not rename architecture fields merely to match the release name.

Keep reasoning history when running multi-turn tool interactions. Provide the tools and workspace required by the task; weights alone do not supply an agent runtime, web credentials, OCR service, or document-processing environment.

Evaluation and limitations

Benchmark results depend on the scaffold, available tools, task coverage, and judge protocol. Internal GDPVal Elo values are not interchangeable with official leaderboard scores. This card does not substitute a single score for those settings. The model can produce incorrect facts, unsafe tool actions, or invalid files. Review outputs and use appropriate sandboxing before executing generated commands.

Attribution and license

This model is derived from Qwen/Qwen3.6-27B. The upstream Apache 2.0 license is retained in LICENSE. The weights were fine-tuned, this model card was replaced, and local provenance fields were removed from release configurations where present. This repository is the GraphForge derivative, not the unmodified upstream model.