Llama 3 8B Instruct 262K by gradientai

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  Arxiv:2305.14233   Arxiv:2309.00071   Arxiv:2402.08268   Autotrain compatible   Conversational   En   Endpoints compatible   Instruct   Llama   Llama-3   Meta   Region:us   Safetensors   Sharded   Tensorflow

Llama 3 8B Instruct 262K Benchmarks

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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:
262144
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:
text
Output Format:
Generated text and code responses.
Performance Tips:
Leverage context window optimizations for handling extensive lengths.
Release Notes 
Version:
5/3 Update
Date:
2024-05-03
Notes:
Finalized assistant-like chat optimizations and context length extension techniques.
LLM NameLlama 3 8B Instruct 262K
Repository ๐Ÿค—https://huggingface.co/gradientai/Llama-3-8B-Instruct-262k 
Model Size8b
Required VRAM16.1 GB
Updated2025-06-09
Maintainergradientai
Model Typellama
Instruction-BasedYes
Model Files  5.0 GB: 1-of-4   5.0 GB: 2-of-4   4.9 GB: 3-of-4   1.2 GB: 4-of-4
Supported Languagesen
Model ArchitectureLlamaForCausalLM
Licensellama3
Context Length262144
Model Max Length262144
Transformers Version4.41.0.dev0
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size128256
Torch Data Typebfloat16
Llama 3 8B Instruct 262K (gradientai/Llama-3-8B-Instruct-262k)

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Note: green Score (e.g. "73.2") means that the model is better than gradientai/Llama-3-8B-Instruct-262k.

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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124