Llama3_1_8B_Finance_QLoRA-merged-16bit

Llama 3.1 8B Financial Sentiment — QLoRA

<p align="center"> <img src="thumbnail.png" alt="Llama 3.1 8B Financial Sentiment QLoRA" width="100%"> </p>

A finance-domain fine-tuned Llama 3.1 8B Instruct model for 3-class financial sentiment classification: negative, neutral, and positive.

The model was fine-tuned with QLoRA, using a 4-bit NF4 frozen base model and BF16 LoRA adapters.

Highlights

• Base: meta-llama/Meta-Llama-3.1-8B-Instruct
• Fine-tuning: QLoRA
• Quantization: 4-bit NF4 during adapter training
• Adapter: LoRA, BF16
• Task: Financial sentiment classification
• Training data: FinGPT/fingpt-sentiment-train (~76k examples)
• License: Apache 2.0

Evaluation compares the fine-tuned model against a zero-shot Meta-Llama-3.1-8B-Instruct baseline using the same prompt template.

Model Details

Quick Start

Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {
        "role": "user",
        "content": "The company's quarterly earnings exceeded analyst expectations. "
                   "The stock price increased following the announcement."
    }
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=50,
        do_sample=False,
    )

response = tokenizer.decode(
    outputs[0][inputs["input_ids"].shape[-1]:],
    skip_special_tokens=True,
)

print(response)

Pipeline

from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit",
)

messages = [
    {
        "role": "user",
        "content": "The company reported record revenue and strong quarterly growth."
    }
]

output = pipe(messages)
print(output)

Intended Use

This model is intended for:

• Financial sentiment analysis
• Research and experimentation with finance-focused LLMs
• Classification of financial news, statements, and market-related text
• Building finance-oriented NLP and GenAI applications

For production financial decision-making, outputs should be independently validated and should not be treated as financial advice.

Limitations

• Model performance depends on the domain and wording of the input.
• Financial language can be ambiguous and context-dependent.
• Benchmark performance does not guarantee production performance.
• The model may produce incorrect or overconfident classifications.
• Human review and application-specific evaluation are recommended for high-impact use cases.

Evaluation

Financial PhraseBank (FPB)

FiQA-SA (Out-of-Domain)

Related Models

• QLoRA adapters: jhon53/Llama3_1_8B_Finance_QLoRA
• GGUF version: jhon53/Llama3_1_8B_Finance_QLoRA-GGUF

Citation

If you use this model in your research or project, please reference the Hugging Face repository:

jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit

Author

Dharmik Bhingradiya

AI/ML Engineer focused on LLM fine-tuning, RAG, model evaluation, MLOps, and production AI systems.

Hugging Face: https://huggingface.co/jhon53