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
```

TeamNull commited on 6 days ago

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@@ -28,10 +28,11 @@ library_name: transformers
 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.
 <div align="center">
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/69c842686cf758859915159c/L4Aa_zxIgHRi_qkIsGrjP.png" width="700">
 </div>
 ## Model Details
 - **Base model:** Qwen3.5-9B
@@ -43,28 +44,6 @@ GCM Mark II is a QLoRA fine-tune of **Qwen3.5-9B**, trained to improve coding re
 ## Intended Use
 General-purpose code generation and coding assistance across multiple languages (Python, JavaScript, Go, C, C++, Java, Rust tested directly). Not evaluated for production/safety-critical code without independent review — see Known Limitations below.
-## Benchmark Results
-Evaluated on a custom 20-question coding eval (mixed languages, weighted toward hard/edge-case problems), hand-graded, single greedy pass, thinking mode enabled. Full methodology, per-question breakdown, and comparison against OpenGCM-v2 and base Qwen3.5-9B are in this repo.
-| Model | Score |
-|---|---|
-| OpenGCM-v2 | 17/20 (85%) |
-| **GCM Mark II** | **16/20 (80%)** |
-| Qwen3.5-9B (base) | 6/20 (30%)* |
-*Base model score includes multiple non-completions rather than purely incorrect answers — see the benchmark for details.
-This comparison is reported as-is, including the result where a competing model scored higher, because the point of publishing it is to be checkable, not to win. n=20 is a small sample — treat these numbers as directional, not definitive, and feel free to reproduce or challenge them.
-**Qualitative note:** GCM Mark II generated responses quickly and reliably across all 20 questions, with no incomplete generations or stuck reasoning loops — worth weighing alongside the raw accuracy numbers if generation reliability matters for your use case.
-## Known Limitations
-- **Directed graph algorithms:** GCM Mark II has a specific, reproducible weakness in cycle detection on directed graphs — it can conflate "visited" with "currently on the active recursion path," causing false-positive cycle detection on some acyclic graphs (e.g. diamond-shaped DAGs). If you're using this model for graph algorithms, verify output independently.
-- Small eval sample (n=20) — broader capability outside the tested question set is not guaranteed.
-- Not evaluated against standardized public benchmarks (HumanEval, LiveCodeBench, MBPP) yet — evaluation attempted but blocked by local tooling issues during development; may be added in a future update.
 ## How to Use

 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.
 <div align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/69c842686cf758859915159c/bfE9PX1C-gomjWpF1JJuE.png" width="700">
 </div>
 ## Model Details
 - **Base model:** Qwen3.5-9B
 ## Intended Use
 General-purpose code generation and coding assistance across multiple languages (Python, JavaScript, Go, C, C++, Java, Rust tested directly). Not evaluated for production/safety-critical code without independent review — see Known Limitations below.
 ## How to Use