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Fireball Alpaca Llama3.1.07 8B Philos Math by EpistemeAI2

 »  All LLMs  »  EpistemeAI2  »  Fireball Alpaca Llama3.1.07 8B Philos Math   URL Share it on

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  Autotrain compatible Base model:epistemeai2/firebal... Base model:finetune:epistemeai...   Conversational   En   Endpoints compatible   Llama   Pytorch   Region:us   Sharded   Trl   Unsloth
Model Card on HF 🤗: https://huggingface.co/EpistemeAI2/Fireball-Alpaca-Llama3.1.07-8B-Philos-Math 

Fireball Alpaca Llama3.1.07 8B Philos Math Benchmarks

MMLU Pro: 28.12
GPQA: 6.15
MUSR: 7.85
BBH: 26.9
IFEval: 50.79 vs 88 (so35)-42.3%
MATH Lvl 5: 11.4
LLME Score: 0.27859
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Fireball Alpaca Llama3.1.07 8B Philos Math (EpistemeAI2/Fireball-Alpaca-Llama3.1.07-8B-Philos-Math)

Fireball Alpaca Llama3.1.07 8B Philos Math Parameters and Internals

Model Type 
text generation, transformers
Use Cases 
Areas:
Research, Commercial applications
Applications:
Assistant-like chat, Natural language generation tasks
Primary Use Cases:
Multilingual dialogue use cases
Limitations:
Use must comply with the Llama 3.1 Community License
Considerations:
Ensure any additional languages and use cases are safe and adhere to guidelines.
Additional Notes 
Designed for openness, inclusivity, and helpfulness in various applications.
Supported Languages 
English (High), German (High), French (High), Italian (High), Portuguese (High), Hindi (High), Spanish (High), Thai (High)
Training Details 
Data Sources:
Publicly available online data
Data Volume:
15T+ tokens
Methodology:
Supervised fine-tuning (SFT), Reinforcement Learning with Human Feedback (RLHF)
Context Length:
128000
Model Architecture:
Auto-regressive transformer architecture
Safety Evaluation 
Methodologies:
Fine-tuning data evaluation, Red teaming
Risk Categories:
Misinformation, Bias
Responsible Ai Considerations 
Transparency:
Models developed following the best practices outlined in Responsible Use Guide.
Accountability:
Developers are expected to ensure safety by implementing internal safeguards.
Mitigation Strategies:
Safety fine-tuning and developing large language model classifiers.
Input Output 
Input Format:
ChatML prompt template, Alpaca prompt
Accepted Modalities:
text
Output Format:
Multilingual textual outputs
Performance Tips:
Use supported languages. Integrate safety guardrails in application setup.
Release Notes 
Version:
1.0
Date:
July 23, 2024
Notes:
This is a static model trained on an offline dataset with multilingual support and extended context length.
LLM NameFireball Alpaca Llama3.1.07 8B Philos Math
Repository 🤗https://huggingface.co/EpistemeAI2/Fireball-Alpaca-Llama3.1.07-8B-Philos-Math 
Base Model(s)  EpistemeAI2/Fireball-Alpaca-Llama3.1-8B-Philos   EpistemeAI2/Fireball-Alpaca-Llama3.1-8B-Philos
Model Size8b
Required VRAM16.1 GB
Updated2024-10-14
MaintainerEpistemeAI2
Model Typellama
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
Gated ModelYes
Model ArchitectureLlamaForCausalLM
Licenseproprietary
Context Length131072
Model Max Length131072
Transformers Version4.44.2
Tokenizer ClassPreTrainedTokenizerFast
Padding Token<|finetune_right_pad_id|>
Vocabulary Size128256
Torch Data Typefloat16

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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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