language:
- en
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
tags:
- code
- python
- educational
- lora
- qwen
library_name: peft
Qwen2.5-Coder-1.5B-Educational (LoRA)
LoRA adapter for Qwen2.5-Coder-1.5B-Instruct fine-tuned on educational code generation.
Quick Start
from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel
Load base model
base_model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen2.5-Coder-1.5B-Instruct", device_map="auto" )
Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "YOUR_USERNAME/qwen-coder-1.5b-educational") tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/qwen-coder-1.5b-educational")
Generate code
prompt = "Instruction: Write a Python function to reverse a string Réponse: " inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=200) print(tokenizer.decode(outputs, skip_special_tokens=True))
Training Details
- Method: LoRA (r=8, alpha=16, dropout=0.05)
- Dataset: OpenCoder-LLM/opc-sft-stage2 (educational_instruct)
- Steps: 2000
- Final Loss: 0.530
- Hardware: TPU v6e-16
- Training Time: 43 minutes
Performance
Improved over base model on:
- Educational Python code generation
- Pythonic idioms and patterns
- Object-oriented architecture
- Code documentation and comments
License
Apache 2.0