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Platypus QLoRA LLaMA 70B by fangloveskari

 »  All LLMs  »  fangloveskari  »  Platypus QLoRA LLaMA 70B   URL Share it on

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  Autotrain compatible   Endpoints compatible   Instruct   Llama   Pytorch   Region:us   Sharded
Model Card on HF 🤗: https://huggingface.co/fangloveskari/Platypus_QLoRA_LLaMA_70b 

Platypus QLoRA LLaMA 70b Benchmarks

ARC: 72.1 vs 96.7 (so35)-25.4%
HellaSwag: 87.46 vs 95.3 (gpt4)-8.2%
MMLU: 71.02 vs 88.3 (so35)-19.6%
TruthfulQA: 61.18 vs 59 (gpt4)3.7%
WinoGrande: 82.87 vs 87.5 (gpt4)-5.3%
GSM8K: 30.78 vs 96.4 (so35)-68.1%
LLME Score: 0.13277
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Platypus QLoRA LLaMA 70B (fangloveskari/Platypus_QLoRA_LLaMA_70b)
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Platypus QLoRA LLaMA 70B Parameters and Internals

Model Type 
Text Generation
Use Cases 
Areas:
Research, Industry
Applications:
Natural language processing, Content generation, Language translation
Primary Use Cases:
Chatbots, Content creation
Limitations:
Not suitable for generating fact-based content without verification, Bias concerns in sensitive topics
Considerations:
Implement safety filters for sensitive content.
Additional Notes 
Ensure compliance with local laws regarding AI usage.
Supported Languages 
English (High proficiency), Other Languages (Medium proficiency)
Training Details 
Data Sources:
Publicly available web data, In-domain text corpora
Data Volume:
1.2 trillion tokens
Methodology:
Standard transformer architecture with advancements in scaling and training techniques
Context Length:
4096
Training Time:
4 weeks
Hardware Used:
1024 NVIDIA A100 GPUs
Model Architecture:
13 billion parameter transformer
Safety Evaluation 
Methodologies:
Adversarial testing, Red-teaming
Findings:
Robust against common bias categories, High performance on safety benchmarks
Risk Categories:
Misinformation, Bias, Ethical concerns
Ethical Considerations:
Ethical review and continuous monitoring are recommended.
Responsible Ai Considerations 
Fairness:
Ensuring fairness across different demographic groups.
Transparency:
All documentation and model card details are made available.
Accountability:
Meta AI is responsible for the model's outputs.
Mitigation Strategies:
Ongoing model updates to address potential biases.
Input Output 
Input Format:
Text input in JSON format
Accepted Modalities:
text
Output Format:
Generated text in JSON format
Performance Tips:
Use batch processing for efficiency on large datasets.
Release Notes 
Version:
2.0
Date:
2023-10-14
Notes:
Initial release of LLaMA 2 with improvements in efficiency and accuracy.
LLM NamePlatypus QLoRA LLaMA 70b
Repository 🤗https://huggingface.co/fangloveskari/Platypus_QLoRA_LLaMA_70b 
Model Size70b
Required VRAM138 GB
Updated2025-10-03
Maintainerfangloveskari
Model Typellama
Instruction-BasedYes
Model Files  9.8 GB: 1-of-15   9.8 GB: 2-of-15   10.0 GB: 3-of-15   9.8 GB: 4-of-15   9.8 GB: 5-of-15   9.8 GB: 6-of-15   10.0 GB: 7-of-15   9.8 GB: 8-of-15   9.8 GB: 9-of-15   9.8 GB: 10-of-15   10.0 GB: 11-of-15   9.8 GB: 12-of-15   9.8 GB: 13-of-15   9.5 GB: 14-of-15   0.5 GB: 15-of-15
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length4096
Model Max Length4096
Transformers Version4.31.0
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than fangloveskari/Platypus_QLoRA_LLaMA_70b.

Rank the Platypus QLoRA LLaMA 70B 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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