FineLlama-3.2-3B-Instruct-ead

This repository contains a fine-tuned version of LLaMa-3.2-3B-Instruct specifically trained to understand and generate EAD (Encoded Archival Description) XML format for archival records description.

Model Description

• Base Model: meta-llama/Llama-3.2-3B-Instruct
• Training Dataset: Geraldine/Ead-Instruct-38k
• Task: Generation of EAD/XML compliant archival descriptions
• Training Type: Instruction fine-tuning with PEFT (Parameter Efficient Fine-Tuning) using LoRA

Key Features

• Specialized in generating EAD/XML format for archival metadata
• Trained on a comprehensive dataset of EAD/XML examples
• Optimized for archival description tasks
• Memory efficient through 4-bit quantization

Training Details

Technical Specifications

• Quantization: 4-bit quantization using bitsandbytes

  • NF4 quantization type
  • Double quantization enabled
  • bfloat16 compute dtype

LoRA Configuration

- r: 256
- alpha: 128
- dropout: 0.05
- target modules: all-linear

Training parameters

- Epochs: 3
- Batch Size: 3
- Gradient Accumulation Steps: 2
- Learning Rate: 2e-4
- Warmup Ratio: 0.03
- Max Sequence Length: 4096
- Scheduler: Constant

Training Infrastructure

• Libraries: transformers, peft, trl
• Mixed Precision: FP16/BF16 (based on hardware support)
• Optimizer: fused adamw

Training Notebook

The training Notebook is available on Kaggle

Usage

Installation

pip install transformers torch bitsandbytes

Loading the model

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

# Configure 4-bit quantization
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

model_name = "Geraldine/FineLlama-3.2-3B-Instruct-ead"

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    quantization_config=bnb_config
).to("cuda")

tokenizer = AutoTokenizer.from_pretrained(model_name)

Example usage

messages = [
  {"role": "system", "content": "You are an expert in EAD/XML generation for archival records metadata."},
  {"role": "user", "content": "Generate a minimal and compliant <eadheader> template with all required EAD/XML tags"},
]

inputs = tokenizer.apply_chat_template(
    messages,
    return_dict=True,
    tokenize = True,
    add_generation_prompt = True, # Must add for generation
    return_tensors = "pt",
).to("cuda")

outputs = model.generate(**inputs, 
                         max_new_tokens = 4096, 
                         pad_token_id=tokenizer.eos_token_id,
                         use_cache = True,)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations

• The model is specifically trained for EAD/XML format and may not perform well on general archival tasks
• Performance depends on the quality and specificity of the input prompts
• Maximum sequence length is limited to 4096 tokens

Citation [optional]

BibTeX:

@misc{ead-llama,
  author = {Géraldine Geoffroy},
  title = {EAD-XML LLaMa: Fine-tuned LLaMa Model for Archival Description},
  year = {2024},
  publisher = {HuggingFace},
  journal = {HuggingFace Repository},
  howpublished = {\url{https://huggingface.co/Geraldine/FineLlama-3.2-3B-Instruct-ead}}
}

Licence

This model is subject to the same license as the base LLaMa model. Please refer to Meta's LLaMa license for usage terms and conditions.