Instructions to use OPENGCM/GCM-MARK-II with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OPENGCM/GCM-MARK-II with Transformers:

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

pipe = pipeline("image-text-to-text", model="OPENGCM/GCM-MARK-II")
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("OPENGCM/GCM-MARK-II")
model = AutoModelForMultimodalLM.from_pretrained("OPENGCM/GCM-MARK-II")
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 OPENGCM/GCM-MARK-II with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OPENGCM/GCM-MARK-II"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OPENGCM/GCM-MARK-II",
		"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/OPENGCM/GCM-MARK-II

SGLang

How to use OPENGCM/GCM-MARK-II 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 "OPENGCM/GCM-MARK-II" \
    --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": "OPENGCM/GCM-MARK-II",
		"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 "OPENGCM/GCM-MARK-II" \
        --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": "OPENGCM/GCM-MARK-II",
		"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 OPENGCM/GCM-MARK-II with Docker Model Runner:
```
docker model run hf.co/OPENGCM/GCM-MARK-II
```

GCM-MARK-II / README.md

TeamNull

Update README.md

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metadata

license: apache-2.0
datasets:
  - ise-uiuc/Magicoder-OSS-Instruct-75K
  - ise-uiuc/Magicoder-Evol-Instruct-110K
language:
  - en
  - zh
  - ja
  - de
  - es
base_model:
  - Qwen/Qwen3.5-9B
tags:
  - gcm
  - qwen3_5
  - reasoning
  - qwen
library_name: transformers

GCM Mark II

GCM Mark II is a QLoRA fine-tune of Qwen3.5-9B, trained to improve coding reliability — specifically constraint-following, edge-case handling, and reducing invented/hallucinated API usage.

Model Details

Base model: Qwen3.5-9B
Fine-tuning method: QLoRA & CPT
Tokens trained: ~2.5 Million
Training data: ise-uiuc/Magicoder-Evol-Instruct-110K (partial epoch)
License: Apache 2.0

Intended Use

General-purpose code generation and coding assistance across multiple backend languages (Python, JavaScript, Go, C, C++, Java, Rust tested directly)
Frontend code generation is not as reliable, future GCM models will work on this more

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
 
model = AutoModelForCausalLM.from_pretrained("OPENGCM/GCM-MARK-II", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("OPENGCM/GCM-MARK-II")
 
messages = [{"role": "user", "content": "Write a function to check if a binary tree is balanced."}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=1024)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Citation / Attribution

Base model: Qwen3.5-9B (Qwen team). Training data: Magicoder-Evol-Instruct-110K (ise-uiuc).

Ollama / GGUF Support

OpenGCM is actively working on .gguf files for quantized versions of GCM Mark II. Stay tuned!