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Tiny Random LlamaForCausalLM by neubla

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  Autotrain compatible   Endpoints compatible   Llama   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/neubla/tiny-random-LlamaForCausalLM 

Tiny Random LlamaForCausalLM Benchmarks

LLME Score: 0.16265
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Tiny Random LlamaForCausalLM (neubla/tiny-random-LlamaForCausalLM)
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Tiny Random LlamaForCausalLM Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
Academic Research, Non-commercial applications
Applications:
Text generation for educational purposes, Creative writing
Primary Use Cases:
Language translation, Summarization
Limitations:
Not suitable for real-time application, High compute cost
Considerations:
Best suited for offline research and use with caution regarding biases.
Additional Notes 
Requires a non-commercial use license from Meta AI.
Training Details 
Data Sources:
Common Crawl, GitHub public repos, Wikipedia
Data Volume:
1.4 trillion tokens
Methodology:
Pretraining using transformer architecture
Context Length:
2048
Training Time:
~35 days using 1024 A100 GPUs
Hardware Used:
NVIDIA A100 GPUs
Model Architecture:
Transformer-based architecture with enhanced efficiency techniques.
Safety Evaluation 
Methodologies:
Adversarial testing, Bias detection
Findings:
Common NLP biases present, improved safety against adversarial attacks
Risk Categories:
misinformation, bias, toxicity
Ethical Considerations:
Addressing known NLP biases and potential misuse.
Responsible Ai Considerations 
Fairness:
Efforts to reduce AI bias incorporated, though challenges remain.
Transparency:
Open sourcing with limited data disclosure for transparency.
Accountability:
Meta AI is accountable for model development.
Mitigation Strategies:
Ongoing evaluation for bias and adversarial robustness.
Input Output 
Input Format:
Textual prompts in natural language.
Accepted Modalities:
text
Output Format:
Generated text in response to input prompts.
Performance Tips:
Ensure high-quality input text to improve output accuracy.
Release Notes 
Version:
v1.0
Date:
2023-06-01
Notes:
Initial release of LLaMA architecture with 70B parameters.
LLM NameTiny Random LlamaForCausalLM
Repository 🤗https://huggingface.co/neubla/tiny-random-LlamaForCausalLM 
Model Size1m
Required VRAM0 GB
Updated2025-09-23
Maintainerneubla
Model Typellama
Model Files  0.0 GB
Model ArchitectureLlamaForCausalLM
Context Length2048
Model Max Length2048
Transformers Version4.42.1
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size32000
Torch Data Typefloat32

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Note: green Score (e.g. "73.2") means that the model is better than neubla/tiny-random-LlamaForCausalLM.

Rank the Tiny Random LlamaForCausalLM 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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