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jk200201
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Model Details
Model Provider
jk200201
Model Tree
Qwen/Qwen2.5-Coder-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Apply the adapter on top of the base with PEFT:
python
import torchfrom peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerbase = "Qwen/Qwen2.5-Coder-7B-Instruct"tok = AutoTokenizer.from_pretrained(base)model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(model, "jk200201/qwen2.5-coder-7b-bird-cot-lora")
If you want a ready-to-run model, use the merged weights or the GGUF build instead (see below). The prompt format and full results are on the merged model card.
| Artifact | Repo |
|---|---|
| Merged model | jk200201/qwen2.5-coder-7b-bird-cot |
| LoRA adapter (this repo) | jk200201/qwen2.5-coder-7b-bird-cot-lora |
| Training data | jk200201/bird-cot-sft |
Reasoning distillation (CoT-SFT) from a Qwen3-Coder-480B teacher, execution-verified. QLoRA rank 32, alpha 64, 2 epochs, learning rate 2e-4 cosine, max sequence length 8192.