CodeMate-Qwen-1.5B-8k 🚀
CodeMate-Qwen-1.5B-8k is a multi-turn, conversational coding assistant built on top of micymike/codemate-qwen-1.5B.
This iteration specifically resolves two critical limitations found in the previous model: it expands the core Context Window to 8,192 tokens using dynamic RoPE scaling, and it completely eliminates the prompt-repetition bug using targeted completion-only loss masking during Supervised Fine-Tuning (SFT).
Model Improvements 🛠️
- Extended Token Window: Upgraded from 2,048 tokens to 8,192 tokens, allowing it to read massive source code files and retain memory across deep, back-and-forth chat debug sessions.
- Prompt Echoing Fixed: Trained natively using Hugging Face TRL's
completion_only_loss=True. The model no longer mimics or repeats user instructions before outputting code. - Bug-Fixing Specialization: Fine-tuned on multi-turn code repair paths (
iamtarun/code_instructions_120k_alpaca), teaching the model how to isolate programming syntax bugs, explain the root issues, and provide production-ready solutions without forgetting its baseline Python and ReactJS knowledge.
Chat Prompt Format (ChatML) 📝
This model utilizes the standard ChatML prompt template structure. To prevent hallucination or parsing drops, structure your inference payloads like this:
<|im_start|>user
[Your Python or React coding/debugging prompt goes here]<|im_end|>
<|im_start|>assistant
Quickstart with Transformers 🐍
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "micymike/codemate-qwen-1.5B-8k"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)
messages = [
{"role": "user", "content": "Fix the bug in this React code where the state hook updates infinitely."}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda")
outputs = model.generate(inputs, max_new_tokens=1024, do_sample=True, temperature=0.3)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Acknowledgements 🎓
Special thanks to the Unsloth AI framework for enabling memory-efficient 8k attention matrix mapping directly inside standard consumer GPU runtimes.