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yukiii02
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Model Details
Model Provider
yukiii02
Model Tree
Qwen/Qwen2.5-7B
Input Modalities
Output Modalities
Supported Functionality
After initial training, the 'Volume 1' model successfully identified syntax errors but struggled with deep Python internals. Volume 2 integrates algorithmic reasoning to solve these gaps.
Volume 2 Improvements:
deepmind/code_contests mapped into RAG format.For optimal coding performance, use the following settings to balance creativity and logical precision:
python
from unsloth import FastLanguageModelmodel, tokenizer = FastLanguageModel.from_pretrained("yukiii02/qwen2.5-7b-flash-bug-hunting-unsloth")FastLanguageModel.for_inference(model) # Recommended Inference Settings for Coding:generation_params = { "max_new_tokens": 512, "temperature": 0.1, # Lower temperature for logical stability "top_p": 0.95, "top_k": 50, "repetition_penalty": 1.1} # Recommended System Prompt:# 'You are an expert bug hunter. Trace the code step-by-step using RAG-style reasoning.'This model was trained using the following resources:
from unsloth import FastLanguageModelmodel, tokenizer = FastLanguageModel.from_pretrained("yukiii02/qwen2.5-7b-flash-bug-hunting-unsloth")FastLanguageModel.for_inference(model) # Recommended Inference Settings for Coding:generation_params = { "max_new_tokens": 512, "temperature": 0.1, # Lower temperature for logical stability "top_p": 0.95, "top_k": 50, "repetition_penalty": 1.1} # Recommended System Prompt:# 'You are an expert bug hunter. Trace the code step-by-step using RAG-style reasoning.'