marysoh/Llama-3.2-1B-Instruct-SFT-Financial-Sentiment
TEXT GENERATIONConcurrency Cost:1Model Size:1BQuant:BF16Ctx Length:32kPublished:Mar 24, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
The marysoh/Llama-3.2-1B-Instruct-SFT-Financial-Sentiment model is a 1 billion parameter instruction-tuned Llama 3.2 variant, developed by marysoh. This model was fine-tuned using Unsloth and Huggingface's TRL library, specifically optimized for financial sentiment analysis. It leverages a 32768 token context length, making it suitable for processing extensive financial texts.
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Model Overview
The marysoh/Llama-3.2-1B-Instruct-SFT-Financial-Sentiment is a 1 billion parameter instruction-tuned model, developed by marysoh. It is based on the Llama 3.2 architecture and was fine-tuned from unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit.
Key Capabilities
- Financial Sentiment Analysis: This model is specifically fine-tuned for tasks related to financial sentiment, making it adept at understanding and classifying sentiment within financial texts.
- Efficient Fine-tuning: The model was trained using Unsloth and Huggingface's TRL library, which enabled 2x faster training.
- Llama 3.2 Architecture: Benefits from the underlying Llama 3.2 instruction-tuned base, providing a strong foundation for language understanding.
- Extended Context Length: Features a 32768 token context window, allowing for the processing of longer financial documents or conversations.
Good For
- Specialized Financial NLP: Ideal for applications requiring nuanced understanding of sentiment in financial news, reports, social media, or earnings call transcripts.
- Resource-Efficient Deployment: As a 1 billion parameter model, it offers a balance between performance and computational efficiency, suitable for deployment in environments with limited resources.
- Research and Development: Provides a solid base for further experimentation and fine-tuning on specific financial datasets.