Llama 3.1 8B Instruct is an open-source language model by meta-llama. Features: 8b LLM, VRAM: 16.1GB, Context: 128K, License: llama3.1, Instruction-Based, LLM Explorer Score: 0.52.
Llama 3.1 8B Instruct Benchmarks
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Llama 3.1 8B Instruct Parameters and Internals
Model Type text generation, multilingual
Use Cases
Areas:
Applications: Assistant-like chat, Multilingual dialogue, Synthetic data generation
Primary Use Cases: Instruction tuning for assistant-like chat
Limitations: Use in unsupported languages without controls, Violations of applicable laws or the Acceptable Use Policy
Considerations: Developers should fine-tune Llama 3.1 models for additional languages responsibly.
Additional Notes Developers can customize model deployment using available recipes and guidelines
Supported Languages en (English), de (German), fr (French), it (Italian), pt (Portuguese), hi (Hindi), es (Spanish), th (Thai)
Training Details
Data Sources: Publicly available online data
Data Volume:
Methodology: supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF)
Context Length:
Training Time:
Hardware Used:
Model Architecture: Auto-regressive language model using an optimized transformer architecture
Safety Evaluation
Methodologies: Safety fine-tuning, Red teaming
Findings: Model must be deployed with system-level safeguards
Risk Categories: Misinformation, Bias, Child Safety, Cybersecurity risks
Ethical Considerations: Avoid using in unsupported languages without fine-tuning and system controls.
Responsible Ai Considerations
Fairness: Focus on multilingual safety and fairness across different languages
Transparency: Clear guidelines and resources provided for deployment
Accountability: Developers must deploy safeguards when building with the model
Mitigation Strategies: Incorporation of safety mitigations, domain-specific evaluations
Input Output
Input Format: Multilingual text and multilingual text with code
Accepted Modalities:
Output Format: Text, including multilingual text and code
Performance Tips: Use transformers or llama codebase for generation
Release Notes
Version:
Date:
Notes: Introduction of multilingual support and longer context window.
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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation
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Release v20241124