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!