RoyYao233/Llama-3.1-8B-Instruct

TEXT GENERATIONPricing:Input $0.2 / Cached $0.028 / Output $0.32Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 18, 2026License:llama3.1Architecture:Transformer Featherless Exclusive Cold

RoyYao233/Llama-3.1-8B-Instruct is an 8 billion parameter instruction-tuned generative language model developed by Meta, part of the Llama 3.1 collection. Optimized for multilingual dialogue use cases, it features an auto-regressive transformer architecture with Grouped-Query Attention and a 128K token context length. This model excels in general language understanding, reasoning, code generation, and mathematical tasks, outperforming many open-source and closed chat models on common industry benchmarks.

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Model Overview

RoyYao233/Llama-3.1-8B-Instruct is an 8 billion parameter instruction-tuned model from Meta's Llama 3.1 family, released on July 23, 2024. It is built on an optimized transformer architecture utilizing Grouped-Query Attention (GQA) for enhanced inference scalability. The model is fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety, specifically targeting multilingual dialogue.

Key Capabilities

  • Multilingual Support: Optimized for 8 languages including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai, with a broader training base for potential fine-tuning in other languages.
  • Extended Context Window: Features a substantial 128K token context length, enabling processing of longer inputs and generating more coherent, extended responses.
  • Enhanced Performance: Demonstrates improved performance over its predecessor, Llama 3 8B Instruct, across various benchmarks, including MMLU (73.0 vs 65.3 CoT), HumanEval (72.6 vs 60.4 pass@1), and MATH (51.9 vs 29.1 final_em).
  • Tool Use: Supports advanced tool use capabilities, allowing integration with external functions and services, with detailed guidance for implementation in transformers and llama frameworks.
  • Robust Training: Pretrained on over 15 trillion tokens of publicly available online data with a knowledge cutoff of December 2023, and fine-tuned with over 25 million synthetically generated examples.

Intended Use Cases

This model is designed for commercial and research applications requiring assistant-like chat functionalities in multiple languages. Its strengths in reasoning, code generation, and mathematical problem-solving make it suitable for a wide range of tasks, from complex query answering to automated code assistance. Developers can also leverage its outputs for improving other models through synthetic data generation and distillation.

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