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Fireball Alpaca Llama3.1.06 8B Philos by EpistemeAI2

 »  All LLMs  »  EpistemeAI2  »  Fireball Alpaca Llama3.1.06 8B Philos   URL Share it on

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  Autotrain compatible Base model:epistemeai2/firebal... Base model:finetune:epistemeai...   En   Endpoints compatible   Instruct   Llama   Pytorch   Region:us   Sharded   Trl   Unsloth
Model Card on HF 🤗: https://huggingface.co/EpistemeAI2/Fireball-Alpaca-Llama3.1.06-8B-Philos 

Fireball Alpaca Llama3.1.06 8B Philos Benchmarks

LLME Score: 0.1736
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Fireball Alpaca Llama3.1.06 8B Philos (EpistemeAI2/Fireball-Alpaca-Llama3.1.06-8B-Philos)
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Fireball Alpaca Llama3.1.06 8B Philos Parameters and Internals

Model Type 
text-generation-inference, question-answering
Use Cases 
Areas:
Commercial use, Research
Applications:
Natural language generation tasks
Primary Use Cases:
Assistant-like chat, Natural language generation tasks
Limitations:
Use in languages beyond those explicitly referenced as supported
Considerations:
Additional languages support requires fine-tuning and compliance with the license terms.
Additional Notes 
Model training was sped up using Unsloth and Huggingface's TRL library.
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
Model Architecture:
Llama 3.1 - auto-regressive language model with transformer architecture
Safety Evaluation 
Methodologies:
red-teaming, adversarial tests
Findings:
Potential outputs cannot be predicted in advance and may include inaccurate or biased responses.
Risk Categories:
misinformation, bias
Ethical Considerations:
Developers should perform safety testing and tuning tailored to their specific model applications.
Responsible Ai Considerations 
Fairness:
Efforts to avoid biases through diverse language support.
Transparency:
Details on data sources and training methods provided.
Accountability:
Developers encouraged to implement system safeguards before deploying.
Mitigation Strategies:
Includes safety fine-tuning and synthesis of quality data.
Input Output 
Input Format:
ChatML prompt template
Accepted Modalities:
text
Output Format:
Multilingual Text and code
Performance Tips:
Usage with the 'transformers' library as specified in instructions.
Release Notes 
Version:
3.1
Date:
July 23, 2024
Notes:
New capabilities include a longer context window, multilingual inputs and outputs.
LLM NameFireball Alpaca Llama3.1.06 8B Philos
Repository 🤗https://huggingface.co/EpistemeAI2/Fireball-Alpaca-Llama3.1.06-8B-Philos 
Base Model(s)  EpistemeAI2/Fireball-Alpaca-Llama3.1-8B-Philos   EpistemeAI2/Fireball-Alpaca-Llama3.1-8B-Philos
Model Size8b
Required VRAM16.1 GB
Updated2025-09-14
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
Model ArchitectureLlamaForCausalLM
Licenseapache-2.0
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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Note: green Score (e.g. "73.2") means that the model is better than EpistemeAI2/Fireball-Alpaca-Llama3.1.06-8B-Philos.

Rank the Fireball Alpaca Llama3.1.06 8B Philos 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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