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Model: micymike/codemate-qwen-1.5B-8k
Source: Original Platform

README.md

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+---
+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.