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Llama 65B by nonlinearshimada

 »  All LLMs  »  nonlinearshimada  »  Llama 65B   URL Share it on

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  Autotrain compatible   Endpoints compatible   Llama   Pytorch   Region:us   Sharded
Model Card on HF 🤗: https://huggingface.co/nonlinearshimada/llama-65b 

Llama 65B Benchmarks

LLME Score: 0.07964
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Llama 65B (nonlinearshimada/llama-65b)
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Llama 65B Parameters and Internals

Model Type 
auto-regressive language model, transformer architecture
Use Cases 
Areas:
research, NLP exploratory tasks
Applications:
question answering, reading comprehension, natural language understanding
Primary Use Cases:
research on large language models, exploring potential applications
Limitations:
has not been trained with human feedback; can thus generate toxic or offensive content
Considerations:
Foundation model, should not be used on downstream applications without further risk evaluation and mitigation.
Supported Languages 
primary (English), others (Spanish, French, German, Dutch, Italian, Portuguese, Russian, Chinese, etc.)
Training Details 
Data Sources:
CCNet, C4, GitHub, Wikipedia, Books, ArXiv, Stack Exchange
Data Volume:
Approximately 1T tokens for smaller models, 1.4T tokens for larger models
Model Architecture:
Transformer
Responsible Ai Considerations 
Fairness:
Expected to reflect biases from sources due to internet data. Evaluated on RAI datasets for various biases.
Mitigation Strategies:
Filtered web data for proximity to Wikipedia with Kneser-Ney language model and fastText linear classifier.
LLM NameLlama 65B
Repository 🤗https://huggingface.co/nonlinearshimada/llama-65b 
Model Size65b
Required VRAM72 GB
Updated2025-06-25
Maintainernonlinearshimada
Model Typellama
Model Files  1.6 GB: 1-of-81   1.6 GB: 2-of-81   1.6 GB: 3-of-81   1.6 GB: 4-of-81   1.6 GB: 5-of-81   1.6 GB: 6-of-81   1.6 GB: 7-of-81   1.6 GB: 8-of-81   1.6 GB: 9-of-81   1.6 GB: 10-of-81   1.6 GB: 11-of-81   1.6 GB: 12-of-81   1.6 GB: 13-of-81   1.6 GB: 14-of-81   1.6 GB: 15-of-81   1.6 GB: 16-of-81   1.6 GB: 17-of-81   1.6 GB: 18-of-81   1.6 GB: 19-of-81   1.6 GB: 20-of-81   1.6 GB: 21-of-81   1.6 GB: 22-of-81   1.6 GB: 23-of-81   1.6 GB: 24-of-81   1.6 GB: 25-of-81   1.6 GB: 26-of-81   1.6 GB: 27-of-81   1.6 GB: 28-of-81   1.6 GB: 29-of-81   1.6 GB: 30-of-81   1.6 GB: 31-of-81   1.6 GB: 32-of-81   1.6 GB: 33-of-81   1.6 GB: 34-of-81   1.6 GB: 35-of-81   1.6 GB: 36-of-81   1.6 GB: 37-of-81   1.6 GB: 38-of-81   1.6 GB: 39-of-81   1.6 GB: 40-of-81   1.6 GB: 41-of-81   1.6 GB: 42-of-81   1.6 GB: 43-of-81   1.6 GB: 44-of-81   1.6 GB: 45-of-81
Model ArchitectureLLaMAForCausalLM
Licenseother
Transformers Version4.27.0.dev0
Vocabulary Size32000
Torch Data Typefloat16

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Llama 65B Hf0K / 72 GB8123
Llama 65B Hf0K / 75.2 GB60
Llama 65B Int40K / 33.5 GB177
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Deepshard 65B Raw0K / 73.6 GB61
Llama 65B 4bit0K / 33.5 GB106
Note: green Score (e.g. "73.2") means that the model is better than nonlinearshimada/llama-65b.

Rank the Llama 65B 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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