shashankyadav03/Llama-3-8B-Instruct-Finance-Asset-Strategy
The shashankyadav03/Llama-3-8B-Instruct-Finance-Asset-Strategy is an 8 billion parameter instruction-tuned Llama 3.1 model developed by Shashank, optimized for multilingual dialogue and natural language generation tasks. This model leverages an optimized transformer architecture, supervised fine-tuning (SFT), and reinforcement learning with human feedback (RLHF) for improved alignment. It excels in various industry benchmarks, including MMLU and HumanEval, and is specifically designed for finance asset advice, supporting multiple languages with a 32768 token context length.
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Model Overview
The shashankyadav03/Llama-3-8B-Instruct-Finance-Asset-Strategy is an 8 billion parameter instruction-tuned model from the Llama 3.1 family, developed by Shashank. Released on August 10, 2024, this model is built upon an optimized transformer architecture and has been fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to enhance its alignment with human preferences, particularly for dialogue.
Key Capabilities and Features
- Multilingual Support: Designed for multilingual dialogue, supporting English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
- Optimized for Finance: The model's name suggests a specialization in finance asset strategy, making it suitable for expert financial advice applications.
- Strong Benchmark Performance: The 8B Instruct version achieves competitive scores, including 69.4 on MMLU (5-shot), 72.6 on HumanEval (pass@1), and 83.4 on ARC-Challenge.
- Tool Use Integration: Supports tool use formats through chat templates within the Transformers library, allowing for custom tool integration.
- Extensive Training: Trained on approximately 15 trillion tokens from publicly available data, with fine-tuning on over 25 million synthetic examples.
Ideal Use Cases
This model is well-suited for commercial and research applications requiring:
- Multilingual Chatbots: Particularly for financial advisory services across supported languages.
- Natural Language Generation: Generating human-like text for various NLP tasks.
- Synthetic Data Generation: Can be used to create synthetic data to improve other models.
- Assistant-like Interfaces: Building conversational AI assistants, especially in the finance domain.