Model Name: LLaMA 3.1 - UnsLoRA Base Model: LLaMA 3.1 Fine-tuning Method: LoRA (Low-Rank Adaptation) using Unsloth Model Type: Large Language Model (LLM) Parameter Count: B8 License: Meta AI’s license
Model Description
LLaMA 3.1 - UnsLoRA is a fine-tuned variant of Meta’s LLaMA 3.1, optimized using LoRA (Low-Rank Adaptation) with Unsloth, a high-speed library designed to accelerate LoRA-based fine-tuning while optimizing memory efficiency. This model is intended for various NLP tasks, including text generation, chatbots, code completion, and domain-specific applications such as therapy-based AI and specialized assistants.
Training Details
Fine-tuning Process
Base Model: LLaMA 3.1 (Pre-trained by Meta AI)
Fine-tuning Method: LoRA using Unsloth
Training Data: RishiKompelli/Therapybotdataset
Epochs: 1
Batch Size: 16
Optimizer: AdamW
Learning Rate: 2e-4
Hardware Used: T4 GPU
Precision: 16-bit (FP16)
Model Capabilities
Natural Language Understanding (NLU): Capable of comprehending complex queries and generating coherent responses.
Conversational AI: Suitable for chatbot applications, including therapy-based AI assistants.
Domain-Specific Adaptation: Fine-tuned with LoRA to specialize in certain tasks, ensuring better contextual awareness.
Low-Rank Adaptation Efficiency: Allows for efficient fine-tuning without requiring full retraining of the model.
Intended Use Cases
AI-powered chatbots for customer support, mental health assistance, and general conversations.
NLP tasks such as text summarization, translation, and question-answering.
Code generation and completion.
Research and experimentation in fine-tuning methodologies using LoRA.
Limitations
Bias & Fairness: Since the model inherits biases from the base LLaMA 3.1 and the fine-tuning data, it may exhibit biases in responses.
Hallucination Risks: The model may generate incorrect or misleading information if the fine-tuning data does not cover a topic adequately.
Ethical Considerations: Usage in sensitive applications like mental health should involve human oversight.
Evaluation Metrics
Perplexity (PPL): 7.6
Deployment & Usage
Frameworks Supported: PyTorch, Hugging Face Transformers
LoRA Integration: Compatible with PEFT (Parameter-Efficient Fine-Tuning)
