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llama316 – AI Model by kevin009 | AlphaNeural AI

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kevin009/
llama316

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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: 50e-6
Batch Size: 2
Number of Epochs: 1
Training Steps: ~2,000
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

python

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