README.md

metadata

license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
  - generated_from_trainer
  - lora
  - qwen
  - code
  - rslora
datasets:
  - custom

qwen2.5-coder-7b-lora-20250713-190211

This model is a fine-tuned version of Qwen/Qwen2.5-Coder-7B-Instruct using LoRA.

Training Details

• Base model: Qwen2.5-Coder-7B-Instruct
• Training method: LoRA with RSLoRA (r=512, alpha=1024)
• Training hardware: NVIDIA RTX 6000 Ada (48GB)
• Training optimizations: Flash Attention 2, BF16, TF32, Sequence Packing
• Training date: 2025-07-13
• Framework: transformers, peft, trl

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Bolito/qwen2.5-coder-7b-lora-20250713-190211")
tokenizer = AutoTokenizer.from_pretrained("Bolito/qwen2.5-coder-7b-lora-20250713-190211")

# Use the model
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Your question here"}
]
text = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)