58 lines
2.5 KiB
Markdown
58 lines
2.5 KiB
Markdown
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---
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license: apache-2.0
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base_model: micymike/codemate-qwen-1.5B
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tags:
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- text-generation
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- coding
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- conversational
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- python
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- react
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- unsloth
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pipeline_tag: text-generation
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library_name: transformers
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language:
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- en
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---
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# CodeMate-Qwen-1.5B-8k 🚀
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CodeMate-Qwen-1.5B-8k is a multi-turn, conversational coding assistant built on top of `micymike/codemate-qwen-1.5B`.
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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).
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### Model Improvements 🛠️
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* **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.
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* **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.
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* **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.
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### Chat Prompt Format (ChatML) 📝
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This model utilizes the standard ChatML prompt template structure. To prevent hallucination or parsing drops, structure your inference payloads like this:
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```text
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<|im_start|>user
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[Your Python or React coding/debugging prompt goes here]<|im_end|>
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<|im_start|>assistant
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```
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### Quickstart with Transformers 🐍
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "micymike/codemate-qwen-1.5B-8k"
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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messages = [
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{"role": "user", "content": "Fix the bug in this React code where the state hook updates infinitely."}
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]
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inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda")
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outputs = model.generate(inputs, max_new_tokens=1024, do_sample=True, temperature=0.3)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### Acknowledgements 🎓
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Special thanks to the [Unsloth AI](https://unsloth.ai) framework for enabling memory-efficient 8k attention matrix mapping directly inside standard consumer GPU runtimes.
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