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Meta Llama 3 8B Instruct Function Calling by Trelis

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  Autotrain compatible   Conversational Dataset:trelis/function callin...   En   Endpoints compatible   Instruct   Llama   Llama 3   Region:us   Safetensors   Sharded   Tensorflow   Trl   Unsloth
Model Card on HF 🤗: https://huggingface.co/Trelis/Meta-Llama-3-8B-Instruct-function-calling 

Meta Llama 3 8B Instruct Function Calling Benchmarks

LLME Score: 0.19302
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 Function Calling (Trelis/Meta-Llama-3-8B-Instruct-function-calling)

Meta Llama 3 8B Instruct Function Calling Parameters and Internals

Model Type 
text-generation, instruct, auto-regressive
Use Cases 
Areas:
research, commercial applications
Applications:
assistant-like chat, NLP tasks
Primary Use Cases:
natural language generation, instruction following
Limitations:
bias and objectionable responses possible, testing primarily in English
Considerations:
Developers should perform safety testing before deploying applications.
Supported Languages 
English (primary)
Training Details 
Data Sources:
public online data, instruction datasets, human-annotated examples
Data Volume:
15 trillion tokens
Methodology:
supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF)
Context Length:
8000
Model Architecture:
optimized transformer architecture
Safety Evaluation 
Methodologies:
red teaming, adversarial evaluations
Findings:
residual risks reduced compared to previous models, emphasis on model refusals to benign prompts
Risk Categories:
CRBNE, Cyber Security, Child Safety
Ethical Considerations:
Model development involved mitigation strategies to limit misuse and harm. Overfocus on safety without impacting user experience.
Responsible Ai Considerations 
Fairness:
Inclusive and open approach.
Transparency:
Responsible Use Guide provided.
Accountability:
Meta provides resources and tools for developers.
Mitigation Strategies:
Purple Llama tools, Llama Guard, and safety best practices
Input Output 
Input Format:
text prompts
Accepted Modalities:
text
Output Format:
text and code
Performance Tips:
Follow safe use guidelines and optimize system level safety with layered tools
LLM NameMeta Llama 3 8B Instruct Function Calling
Repository 🤗https://huggingface.co/Trelis/Meta-Llama-3-8B-Instruct-function-calling 
Model Size8b
Required VRAM16.1 GB
Updated2025-02-22
MaintainerTrelis
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
Licenseapache-2.0
Context Length8192
Model Max Length8192
Transformers Version4.40.0
Tokenizer ClassPreTrainedTokenizerFast
Padding Token<|end_of_text|>
Vocabulary Size128256
Torch Data Typebfloat16

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

Rank the Meta Llama 3 8B Instruct Function Calling Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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 v20241227
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