OPENGCM/GCM-MARK-II

<div align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/69c842686cf758859915159c/EfCpXkcGtQhv02oKAy2La.png" width="700"> </div>

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.

<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
• —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!