Llama 3 8B Instruct 262K is an open-source language model by gradientai. Features: 8b LLM, VRAM: 16.1GB, Context: 256K, License: llama3, Instruction-Based, HF Score: 60.3, LLM Explorer Score: 0.2, Arc: 53.2, HellaSwag: 75.5, MMLU: 64.3, TruthfulQA: 48.4, WinoGrande: 73, GSM8K: 47.2.
Llama 3 8B Instruct 262K 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 8B Instruct 262K Parameters and Internals
Model Type text generation, dialogue, instruction tuned
Use Cases
Areas: Commercial use, Research use
Applications: Dialog and assistant-like applications
Primary Use Cases: Assistant-like chat and instruction tasks
Limitations: Inapplicable in languages other than English without additional tuning., Requires appropriate safety tuning for specialized applications.
Considerations: Developers should employ additional safeguarding measures and consider context when deploying applications.
Additional Notes Quantized versions available for resource-efficient deployments.
Supported Languages en (Primary support for dialogue and instructional tasks in English.)
Training Details
Data Sources: publicly available online data, SlimPajama-627B, UltraChat
Data Volume: 15 trillion tokens (pretraining)
Methodology: Supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF). NTK-aware interpolation technique for context length adjustment.
Context Length:
Training Time: Total of 7.7M GPU hours for pretraining across multiple models.
Hardware Used: Crusoe Energy high performance L40S cluster
Model Architecture: Optimized transformer using RoPE theta for extended contexts.
Safety Evaluation
Methodologies: red-teaming, adversarial evaluations
Findings: Improved refusal rate for false prompts in comparison to Llama 2., Limited residual risks assessed through community tools.
Risk Categories: Child safety, Cybersecurity vulnerabilities, CBRNE threats
Ethical Considerations: Guided by a Responsible Use Guide and supporting tools like Meta Llama Guard 2.
Responsible Ai Considerations
Fairness: Focused on openness, inclusivity, and helpfulness while respecting diverse values and perspectives.
Transparency: Incorporates feedback from the community to continually assess safety and alignment.
Accountability: Developers undertaking use are held to the standards under the Acceptable Use Policy.
Mitigation Strategies: Use community safeguards like Llama Guard to supplement model-level safety.
Input Output
Input Format: Text input following a conversational template.
Accepted Modalities:
Output Format: Generated text and code responses.
Performance Tips: Leverage context window optimizations for handling extensive lengths.
Release Notes
Version:
Date:
Notes: Finalized assistant-like chat optimizations and context length extension techniques.
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Note: green Score (e.g. "73.2 ") means that the model is better than gradientai/Llama-3-8B-Instruct-262k .
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Rank the Llama 3 8B Instruct 262K Capabilities
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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 v20260328a