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Llama 3 8B Instruct Gradient 1048K by gradientai

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  Arxiv:2305.14233   Arxiv:2309.00071   Arxiv:2402.08268   Autotrain compatible   Conversational   Doi:10.57967/hf/3372   En   Endpoints compatible   Instruct   Llama   Llama-3   Meta   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k 

Llama 3 8B Instruct Gradient 1048K Benchmarks

MMLU Pro: 21.56
GPQA: 3.69
MUSR: 13.52
BBH: 21.01
IFEval: 44.56 vs 88 (so35)-49.4%
ARC: 54.44 vs 96.7 (so35)-43.7%
HellaSwag: 76.77 vs 95.3 (gpt4)-19.4%
MMLU: 61.92 vs 88.3 (so35)-29.9%
TruthfulQA: 49.26 vs 59 (gpt4)-16.5%
WinoGrande: 72.3 vs 87.5 (gpt4)-17.4%
GSM8K: 44.35 vs 96.4 (so35)-54%
MATH Lvl 5: 5.14
LLME Score: 0.39233
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 Gradient 1048K (gradientai/Llama-3-8B-Instruct-Gradient-1048k)

Llama 3 8B Instruct Gradient 1048K Parameters and Internals

Model Type 
text-generation
Use Cases 
Areas:
commercial, research
Applications:
natural language generation
Primary Use Cases:
assistant-like chat
Limitations:
not suitable for use in languages other than English
Considerations:
developers to perform safety testing and tuning tailored to applications
Additional Notes 
Model is static and trained on an offline dataset. Future versions will focus on safety improvements.
Supported Languages 
en (primary)
Training Details 
Data Sources:
publicly available online data, SlimPajama, UltraChat
Data Volume:
1.4B tokens total
Methodology:
NTK-aware interpolation for RoPE theta optimization, progressive training on increasing context lengths, supervised fine-tuning (SFT), reinforcement learning with human feedback (RLHF)
Context Length:
1048
Hardware Used:
Crusoe Energy high performance L40S cluster
Model Architecture:
auto-regressive language model using an optimized transformer architecture
Safety Evaluation 
Methodologies:
red teaming, adversarial evaluations
Findings:
mitigations implemented to limit false refusals, CBRNE assessments
Risk Categories:
misuse, critical risks, cybersecurity, child safety
Ethical Considerations:
open approach to better, safer products, emphasis on responsible AI development
Responsible Ai Considerations 
Fairness:
openness, inclusivity, helpfulness
Transparency:
steps and best practices for safe deployment
Accountability:
developers
Mitigation Strategies:
Purple Llama solutions, Llama Guard for input-output safeguards
Input Output 
Input Format:
text only
Accepted Modalities:
text
Output Format:
text and code only
LLM NameLlama 3 8B Instruct Gradient 1048K
Repository 🤗https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k 
Model Size8b
Required VRAM16.1 GB
Updated2024-12-21
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 Length1048576
Model Max Length1048576
Transformers Version4.41.0.dev0
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size128256
Torch Data Typebfloat16

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Rank the Llama 3 8B Instruct Gradient 1048K 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 v20241217
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