MaziyarPanahi/Llama-3-8B-Instruct-v0.10
MaziyarPanahi/Llama-3-8B-Instruct-v0.10 is an 8 billion parameter instruction-tuned causal language model developed by MaziyarPanahi, based on the Llama 3 architecture. This model is an iteration of the Llama-3-8B-Instruct-v0.9, optimized for conversational AI and general instruction following tasks. It features an 8192-token context length and utilizes the ChatML prompt template, making it suitable for various interactive applications.
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Model Overview
MaziyarPanahi/Llama-3-8B-Instruct-v0.10 is an instruction-tuned large language model built upon the Llama 3 architecture, featuring 8 billion parameters. This version is an enhancement over its predecessor, Llama-3-8B-Instruct-v0.9, developed by MaziyarPanahi.
Key Capabilities & Features
- Instruction Following: Optimized for understanding and executing a wide range of user instructions.
- Conversational AI: Designed for interactive chat applications, leveraging the ChatML prompt template for structured dialogues.
- Context Length: Supports an 8192-token context window, allowing for more extensive conversations and complex prompts.
- Quantized GGUF: Available in quantized GGUF formats for efficient deployment and inference on various hardware.
Performance Highlights
Evaluated on the Open LLM Leaderboard, the model achieved an average score of 26.66. Notable scores include 76.67 on IFEval (0-Shot) and 31.80 on MMLU-PRO (5-shot), indicating its proficiency in instruction understanding and general knowledge tasks.
Usage
This model can be readily integrated using the Hugging Face transformers library, with provided Python code examples demonstrating how to set up the pipeline for text generation and apply the ChatML prompt template.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.