README.md

metadata

library_name: transformers
tags:
  - unsloth
  - trl
  - sft

Model Card for Fin-LLaMA 3.1 8B

This is the model card for Fin-LLaMA 3.1 8B, a fine-tuned version of LLaMA 3.1 trained specifically on financial news data. The model is built to generate coherent and relevant financial, economic, and business text responses. It also includes multiple quantized GGUF model formats for resource-efficient deployment.

Model Details

Model Description

The Fin-LLaMA 3.1 8B model was fine-tuned using the Unsloth library, employing LoRA adapters for efficient training, and is available in various quantized GGUF formats. The model is instruction-tuned to generate text in response to finance-related queries.

• Developed by: us4
• Model type: Transformer (LLaMA 3.1 architecture, 8B parameters)
• Languages: English
• License: [More Information Needed]
• Fine-tuned from model: LLaMA 3.1 8B

Files and Formats

The repository contains multiple files, including safetensors and GGUF formats for different quantization levels. Below is the list of key files and their details:

• adapter_config.json (778 Bytes): Configuration for the adapter model.
• adapter_model.safetensors (5.54 GB): Adapter model in safetensors format.
• config.json (978 Bytes): Model configuration file.
• generation_config.json (234 Bytes): Generation configuration file for text generation.
• model-00001-of-00004.safetensors (4.98 GB): Part 1 of the model in safetensors format.
• model-00002-of-00004.safetensors (5.00 GB): Part 2 of the model in safetensors format.
• model-00003-of-00004.safetensors (4.92 GB): Part 3 of the model in safetensors format.
• model-00004-of-00004.safetensors (1.17 GB): Part 4 of the model in safetensors format.
• model-q4_0.gguf (4.66 GB): Quantized GGUF format (Q4_0).
• model-q4_k_m.gguf (4.92 GB): Quantized GGUF format (Q4_K_M).
• model-q5_k_m.gguf (5.73 GB): Quantized GGUF format (Q5_K_M).
• model-q8_0.gguf (8.54 GB): Quantized GGUF format (Q8_0).
• model.safetensors.index.json (24 KB): Index file for the safetensors model.
• special_tokens_map.json (454 Bytes): Special tokens mapping file.
• tokenizer.json (9.09 MB): Tokenizer configuration for the model.
• tokenizer_config.json (55.4 KB): Additional tokenizer settings.
• training_args.bin (5.56 KB): Training arguments used for fine-tuning.

GGUF Formats and Usage

The GGUF formats are optimized for memory-efficient inference, especially for edge devices or deployment in low-resource environments. Here’s a breakdown of the quantized GGUF formats available:

• Q4_0: 4-bit quantized model for high memory efficiency with some loss in precision.
• Q4_K_M: 4-bit quantized with optimized configurations for maintaining precision.
• Q5_K_M: 5-bit quantized model balancing memory efficiency and accuracy.
• Q8_0: 8-bit quantized model for higher precision with a larger memory footprint.

GGUF files available in the repository:

• model-q4_0.gguf (4.66 GB)
• model-q4_k_m.gguf (4.92 GB)
• model-q5_k_m.gguf (5.73 GB)
• model-q8_0.gguf (8.54 GB)

To load and use these GGUF models for inference:

from unsloth import FastLanguageModel
#
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="us4/fin-llama3.1-8b", 
    max_seq_length=2048,
    load_in_4bit=True,  # Set to False for Q8_0 format
    quantization_method="q4_k_m"  # Change to the required format (e.g., "q5_k_m" or "q8_0")
)

Model Sources

• Repository: Fin-LLaMA 3.1 8B on Hugging Face
• Paper: LLaMA: Open and Efficient Foundation Language Models

Uses

The Fin-LLaMA 3.1 8B model is designed for generating business, financial, and economic-related text.

Direct Use

The model can be directly used for text generation tasks, such as generating financial news summaries, analysis, or responses to finance-related prompts.

Downstream Use

The model can be further fine-tuned for specific financial tasks, such as question-answering systems, summarization of financial reports, or automation of business processes.

Out-of-Scope Use

The model is not suited for use in domains outside of finance, such as medical or legal text generation, nor should it be used for tasks that require deep financial forecasting or critical decision-making without human oversight.

Bias, Risks, and Limitations

The model may inherit biases from the financial news data it was trained on. Since financial reporting can be region-specific and company-biased, users should exercise caution when applying the model in various international contexts.

Recommendations

Users should carefully evaluate the generated text in critical business or financial settings. Ensure the generated content aligns with local regulations and company policies.

Training Details

Training Data

The model was fine-tuned on a dataset of financial news articles, consisting of titles and content from various financial media sources. The dataset has been pre-processed to remove extraneous information and ensure consistency across financial terms.

Training Procedure

Preprocessing

The training data was tokenized using the LLaMA tokenizer, with prompts formatted to include both the title and content of financial news articles.

Training Hyperparameters

• Training regime: Mixed precision (FP16), gradient accumulation steps: 8, max steps: 500.
• Learning Rate: 5e-5 for fine-tuning, 1e-5 for embeddings.
• Batch size: 8 per device.

Speeds, Sizes, Times

The model training took place over approximately 500 steps on an A100 GPU. Checkpoint files range from 4.98 GB to 8.54 GB depending on quantization.

Evaluation

Testing Data, Factors & Metrics

Testing Data

The model was tested on unseen financial news articles from the same source domains as the training set.

Factors

Evaluation focused on the model’s ability to generate coherent financial summaries and responses.

Metrics

Common text-generation metrics such as perplexity, accuracy in summarization, and human-in-the-loop evaluations were used.

Results

The model demonstrated strong performance in generating high-quality financial text. It maintained coherence over long sequences and accurately represented financial data from the prompt.

Model Examination

No interpretability techniques have yet been applied to this model, but explainability is under consideration for future versions.

Environmental Impact

Training carbon emissions can be estimated using the Machine Learning Impact calculator.

• Hardware Type: A100 GPU
• Hours used: Approximately 72 hours for fine-tuning
• Cloud Provider: AWS
• Compute Region: US-East
• Carbon Emitted: Estimated at 43 kg of CO2eq

Technical Specifications

Model Architecture and Objective

The Fin-LLaMA 3.1 8B model is based on the LLaMA 3.1 architecture and uses LoRA adapters to efficiently fine-tune the model on financial data.

Compute Infrastructure

The model was trained on A100 GPUs using PyTorch and the Hugging Face 🤗 Transformers library.

Hardware

• GPU: A100 (80GB)
• Storage Requirements: Around 20GB for the fine-tuned checkpoints, depending on quantization format.

Software

• Library: Hugging Face Transformers, Unsloth, PyTorch, PEFT
• Version: Unsloth v1.0, PyTorch 2.0, Hugging Face Transformers 4.30.0

Citation

If you use this model in your research or applications, please consider citing:

BibTeX:

@article{touvron2023llama,
  title={LLaMA: Open and Efficient Foundation Language Models},
  author={Touvron, Hugo and others},
  journal={arXiv preprint arXiv:2302.13971},
  year={2023}
}
@misc{us4_fin_llama3_1,
  title={Fin-LLaMA 3.1 8B - Fine-tuned on Financial News},
  author={us4},
  year={2024},
  howpublished={\url{https://huggingface.co/us4/fin-llama3.1-8b}},
}

More Information

For any additional information, please refer to the repository or contact the authors via the Hugging Face Hub.

Model Card Contact

[More Information Needed]