devshaheen/unsloth-finetuning-4bit-mistral_imdb

Uploaded Model

• Developed by: Shaheen Nabi
• License: Apache-2.0
• Finetuned from model: unsloth/mistral-7b-bnb-4bit
• Model Type: Large Language Model (LLM)
• Training Framework: Hugging Face Transformers, TRL (Transformers Reinforcement Learning) library
• Pretraining Dataset: Stanford IMDb Dataset
• Fine-Tuning Dataset: Stanford IMDb (Text Classification Task)

Overview

This model is a fine-tuned version of unsloth/mistral-7b-bnb-4bit, a 7-billion-parameter model based on the Mistral architecture. It was fine-tuned to improve performance on natural language understanding tasks, specifically for text classification using the Stanford IMDb dataset.

The fine-tuning process leveraged the Unsloth framework, which significantly sped up the training time, enabling a 2x faster training process. Additionally, Hugging Face's TRL library (Transformers Reinforcement Learning) was used to adapt the model efficiently.

Training Details

• Base Model: unsloth/mistral-7b-bnb-4bit (7B parameters, 4-bit quantized weights for memory efficiency)
• Training Speed: The model was trained 2x faster with Unsloth, optimizing training time and resource usage.
• Optimization Techniques: Low-rank adaptation (LoRA), gradient checkpointing, and 4-bit quantization were employed to reduce memory and computational cost while maintaining model performance.

Intended Use

This model is designed for tasks such as:

• Sentiment analysis
• Text classification
• Fine-grained NLP tasks

It is optimized for deployment in resource-constrained environments due to the quantization of the base model and fine-tuning techniques used.

Model Performance

• Primary Metric: Accuracy on text classification tasks (Stanford IMDb dataset)
• Fine-Tuning Results: The fine-tuned model shows improved accuracy, making it a practical choice for NLP applications.

Usage

To use the model, you can load it using the FastLanguageModel class as follows:

from unsloth import FastLanguageModel

# Load the fine-tuned model and tokenizer
model_name = "shaheennabi/your-finetuned-mistral-7b-imdb"
max_seq_length = 512  # Set according to your requirements

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=model_name,
    max_seq_length=max_seq_length,
    dtype=None,
    load_in_4bit=True
)

# Example of using the model for inference
input_text = "This movie was fantastic!"
inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
outputs = model(**inputs)