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micymike
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Model Details
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micymike
Model Tree
micymike/codemate-qwen-1.5B
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Output Modalities
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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.
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).
completion_only_loss=True. The model no longer mimics or repeats user instructions before outputting code.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.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|>assistantpython
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))Special thanks to the Unsloth AI framework for enabling memory-efficient 8k attention matrix mapping directly inside standard consumer GPU runtimes.
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))