kevin009/llama313

Model Overview

A LoRA (Low-Rank Adaptation) fine-tuned adapter for the Llama-3.1-8B language model.

Model Details

• Base Model: meta-llama/Llama-3.1-8B-instruct
• Adaptation Method: LoRA

Training Configuration

Training Hyperparameters

• Learning Rate: 85e-6
• Batch Size: 2
• Number of Epochs: 1
• Training Steps: ~1,400
• Precision: "BF16"

LoRA Configuration

• Rank (r): 16
• Alpha: 16
• Target Modules:
  • q_proj (Query projection)
  • k_proj (Key projection)
  • v_proj (Value projection)
  • o_proj (Output projection)
  • up_proj (Upsampling projection)
  • down_proj (Downsampling projection)
  • gate_proj (Gate projection)

Usage

This adapter must be used in conjunction with the base Llama-3.1-8B model.

Loading the Model

from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-instruct")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-instruct")

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

Limitations and Biases

• This adapter might inherits some limitations and biases present in the base Llama-3.1-8B-instruct model
• The training dataset size (~1k steps) is relatively small, which may limit the adapter's effectiveness