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Meta Llama 3 8B Instruct Ct2 Int8 by jncraton

 »  All LLMs  »  jncraton  »  Meta Llama 3 8B Instruct Ct2 Int8   URL Share it on

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  Conversational   En   Endpoints compatible   Facebook   Instruct   Llama   Llama-3   Meta   Pytorch   Region:us
Model Card on HF 🤗: https://huggingface.co/jncraton/Meta-Llama-3-8B-Instruct-ct2-int8 

Meta Llama 3 8B Instruct Ct2 Int8 Benchmarks

LLME Score: 0.19023
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Meta Llama 3 8B Instruct Ct2 Int8 (jncraton/Meta-Llama-3-8B-Instruct-ct2-int8)

Meta Llama 3 8B Instruct Ct2 Int8 Parameters and Internals

Model Type 
text generation, instruction tuned
Use Cases 
Areas:
Commercial, Research
Applications:
Instruction tuned models for assistant-like chat, Pretrained models for various natural language tasks
Primary Use Cases:
English language applications
Limitations:
Not for use in languages other than English, Requires adherence to the Use Policy and Llama 3 Community License
Considerations:
Developers may fine-tune for additional languages within license compliance.
Additional Notes 
Llama 3 is designed with openness, inclusivity, and helpfulness as core values. Testing is primarily in English, with certain potential risks and uncertainties.
Supported Languages 
English (high)
Training Details 
Data Sources:
publicly available online data
Data Volume:
15T+ tokens for pretraining, over 10M human-annotated examples for fine-tuning
Methodology:
Auto-regressive language model using an optimized transformer architecture, supervised fine-tuning and reinforcement learning with human feedback (RLHF)
Context Length:
8000
Hardware Used:
Meta's Research SuperCluster, H100-80GB GPUs
Model Architecture:
Auto-regressive language model with optimized transformer architecture
Safety Evaluation 
Methodologies:
Red teaming, Adversarial evaluations, CyberSecEval
Findings:
Equivalent or safer than models with similar coding capabilities
Risk Categories:
CBRNE threats, Cyber attacks, Child safety risks
Ethical Considerations:
Responsible AI development with safety benchmarks, iterative testing during model training, and community involvement.
Responsible Ai Considerations 
Transparency:
Uses Responsible Use Guide and tools like Meta Llama Guard 2 for transparency.
Accountability:
Meta and developers share responsibilities to avoid bias and enhance safety.
Mitigation Strategies:
Supervised fine-tuning and reinforcement learning with human feedback to align with preferences.
Input Output 
Input Format:
text only
Accepted Modalities:
text
Output Format:
text and code only
LLM NameMeta Llama 3 8B Instruct Ct2 Int8
Repository 🤗https://huggingface.co/jncraton/Meta-Llama-3-8B-Instruct-ct2-int8 
Model Size8b
Required VRAM8 GB
Updated2024-12-22
Maintainerjncraton
Instruction-BasedYes
Model Files  8.0 GB
Supported Languagesen
Model ArchitectureAutoModel
Licenseother
Tokenizer ClassPreTrainedTokenizerFast

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Note: green Score (e.g. "73.2") means that the model is better than jncraton/Meta-Llama-3-8B-Instruct-ct2-int8.

Rank the Meta Llama 3 8B Instruct Ct2 Int8 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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