qwen2-7b-battery-lora

Qwen2-7B Battery Domain LoRA (Learning Project)

This is a LoRA fine-tuned Qwen2-7B model trained on a small custom battery-related dataset (232 Q&A pairs).
The goal of this project is to learn how to fine-tune large language models using Unsloth and LoRA adapters.

Note: This model is part of my learning phase and not meant for production use.
It demonstrates end-to-end fine-tuning, saving, and sharing on Hugging Face.

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Model Details

• Base Model: Qwen/Qwen2-7B-Instruct
• Fine-tuning framework: Unsloth
• Precision: 4-bit quantization with LoRA adapters
• Training data: 232 rows of domain-specific Q&A pairs related to battery management ICs
• LoRA rank: 16
• Epochs: 2
• Trainable parameters: ~0.53% (~40M params out of 7.6B)

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Training Objective

• Learn how to structure datasets for instruction tuning
• Practice low-VRAM LoRA fine-tuning on a Tesla T4 GPU
• Upload and version control models on Hugging Face Hub

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Limitations

Very small dataset → the model may not generalize well

Answers could be inaccurate or hallucinated outside the training scope

Not evaluated on a formal benchmark yet

Inference Example

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="your-username/qwen2-7b-battery-lora",
    max_seq_length=2048,
    load_in_4bit=True,
    device_map="auto"
)

prompt = """Below is an instruction that describes a task.

### Instruction:
What does ManufacturerAccess(0x57) indicate?

### Input:


### Response:
"""
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
'''