metadata
base_model: meta-llama/meta-llama-3.1-8b-instruct
tags:
- llama adapter
- trl
- llama3.1 8b
license: apache-2.0
language:
- en
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: 25e-6
- Batch Size: 2
- Number of Epochs: 2
- Training Steps: ~3,000
- 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
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