GRPO_LoRA_Qwen_3B

Qwen2.5-3B-Instruct with LoRA Adapter

This model is a fine-tuned version of the unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit using Parameter-Efficient Fine-Tuning (PEFT) with the LoRA method.

Model Details

Model Description

This model applies LoRA (Low-Rank Adaptation) to the Qwen2.5-3B-Instruct base model, targeting key projection modules for efficient fine-tuning. It is quantized to 4-bit precision using the Unsloth library to optimize for inference performance on lower-resource hardware.

• Developed by: montebello.ai
• Funded by: Bootstrapped 4 Life
• Model type: Causal Language Model with LoRA Adapter
• Language(s) (NLP): English (primary), multilingual support for some other languages (research ongoing).
• License: MIT
• Finetuned from model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit

Model Sources

• Demo [optional]: [COMING SOON]

Uses

Direct Use

This model is designed for natural language understanding and generation tasks, including:

• Conversational AI
• Summarization
• Text completion
• Question answering

Downstream Use

Fine-tuning the model for specific NLP tasks, such as custom domain conversational agents.

Out-of-Scope Use

• Real-time critical systems requiring guaranteed safety and accuracy.
• Generating content for sensitive domains without human oversight.

Bias, Risks, and Limitations

• The base model may contain biases present in the original training data.
• The model is not fine-tuned for safety-critical applications.
• Limitations include possible hallucinations in generative outputs and language biases.

Recommendations

• Perform task-specific evaluations before deploying the model.
• Include human-in-the-loop for critical applications.

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

tokenizer = AutoTokenizer.from_pretrained("unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit")
base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "path_to_your_adapter")

# Example inference
input_text = "Your input text here."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))

Training Details

Training Procedure

• Preprocessing: Tokenization with AutoTokenizer.
• Training regime: bf16 mixed precision with LoRA fine-tuning.
• LoRA Configuration:
  • lora_alpha: 64
  • r: 64
  • target_modules: ['v_proj', 'o_proj', 'up_proj', 'gate_proj', 'down_proj', 'q_proj', 'k_proj']
  • bias: none

Training Data

This model was trained on the GSM8K dataset from OpenAI.

Environmental Impact

• Hardware Type: T4 TPU
• Hours used: 2.5 hours

Technical Specifications

Model Architecture and Objective

• Transformer-based causal language model using LoRA for efficient adaptation.

Compute Infrastructure

• Hardware: [Specify GPU/CPU setup]
• Software:
  • Python 3.x
  • Transformers library
  • PEFT 0.14.0

Citation [optional]

BibTeX:

@misc{your2025model,
  title={Qwen2.5-3B-Instruct with LoRA Adapter},
  author={Kenneth Hamilton},
  year={2025}
}

APA:

Your Name. (2025). Qwen2.5-3B-Instruct with LoRA Adapter. Retrieved from Your repository link.

Model Card Contact

• Contact: [Your contact information]

Framework versions

• PEFT 0.14.0