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Fireball Alpaca Llama3.1 8B Philos by EpistemeAI2

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  4bit   Autotrain compatible Base model:finetune:unsloth/me... Base model:unsloth/meta-llama-...   Conversational   En   Endpoints compatible   Instruct   Llama   Model-index   Pytorch   Quantized   Region:us   Sharded   Trl   Unsloth
Model Card on HF 🤗: https://huggingface.co/EpistemeAI2/Fireball-Alpaca-Llama3.1-8B-Philos 

Fireball Alpaca Llama3.1 8B Philos Benchmarks

MMLU Pro: 26.73
GPQA: 5.7
MUSR: 11.89
BBH: 29.26
IFEval: 49.86 vs 88 (so35)-43.3%
MATH Lvl 5: 11.78
LLME Score: 0.27687
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 8B Philos (EpistemeAI2/Fireball-Alpaca-Llama3.1-8B-Philos)

Fireball Alpaca Llama3.1 8B Philos Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
Research, Commercial applications
Applications:
Assistant-like chat, Natural language generation tasks, Synthetic data generation and distillation
Primary Use Cases:
Multilingual dialogue, Commercial and research use
Limitations:
Use in unsupported languages is discouraged.
Considerations:
Ensure responsible and safe use in alignment with best practices.
Additional Notes 
Note the special conditions for multilingual support beyond the initially tested languages.
Supported Languages 
English (High proficiency), German (High proficiency), French (High proficiency), Italian (High proficiency), Portuguese (High proficiency), Hindi (High proficiency), Spanish (High proficiency), Thai (High proficiency)
Training Details 
Data Sources:
A new mix of publicly available online data
Data Volume:
15T+ tokens
Methodology:
Supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF).
Context Length:
128000
Training Time:
2x faster with Unsloth and Huggingface's TRL library
Hardware Used:
Unsloth tool and Hugging Face TRL library
Model Architecture:
Auto-regressive language model using an optimized transformer architecture.
Safety Evaluation 
Methodologies:
Red teaming, Adversarial testing
Risk Categories:
Misinformation, Bias, CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials), Child Safety, Cyber attack enablement
Ethical Considerations:
Model may produce inaccurate, biased or objectionable responses. Safety testing and tuning are required before deployment.
Responsible Ai Considerations 
Fairness:
Model is intended to serve a diverse range of users without normative constraints.
Transparency:
Guidelines for ethical and safe deployment are available.
Accountability:
Developers are responsible for evaluating their specific applications.
Mitigation Strategies:
Includes safety tuning, adversarial testing, and compliance with a Responsible Use Guide.
Input Output 
Input Format:
ChatML and Alpaca prompts
Accepted Modalities:
text
Output Format:
Multilingual text and code
Performance Tips:
Use dedicated prompt templates for better performance.
Release Notes 
Version:
3.1
Date:
July 23, 2024
Notes:
Released with custom commercial license. Community feedback to improve model safety.
LLM NameFireball Alpaca Llama3.1 8B Philos
Repository 🤗https://huggingface.co/EpistemeAI2/Fireball-Alpaca-Llama3.1-8B-Philos 
Base Model(s)  unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit   unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
Model Size8b
Required VRAM16.1 GB
Updated2024-12-09
MaintainerEpistemeAI2
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
Gated ModelYes
Quantization Type4bit
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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Rank the Fireball Alpaca Llama3.1 8B Philos 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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