---
license: apache-2.0
base_model: micymike/codemate-qwen-1.5B
tags:
- text-generation
- coding
- conversational
- python
- react
- unsloth
pipeline_tag: text-generation
library_name: transformers
language:
- en
---

# 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:

```text
<|im_start|>user
[Your Python or React coding/debugging prompt goes here]<|im_end|>
<|im_start|>assistant
```

### Quickstart with Transformers 🐍

```python
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](https://unsloth.ai) framework for enabling memory-efficient 8k attention matrix mapping directly inside standard consumer GPU runtimes.