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Mamba 2.8B Chat No Robots by clibrain

 »  All LLMs  »  clibrain  »  Mamba 2.8B Chat No Robots   URL Share it on

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  Arxiv:2203.02155   Conversational Dataset:huggingfaceh4/no robot...   En   Endpoints compatible   Pytorch   Region:us   Tensorboard
Model Card on HF 🤗: https://huggingface.co/clibrain/mamba-2.8b-chat-no_robots 

Mamba 2.8B Chat No Robots Benchmarks

LLME Score: 0.12887
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mamba 2.8B Chat No Robots (clibrain/mamba-2.8b-chat-no_robots)
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Mamba 2.8B Chat No Robots Parameters and Internals

Model Type 
text generation
Additional Notes 
The model is inspired by Mamba state space model architecture with focus on efficient handling of information-dense data.
Supported Languages 
English (en)
Training Details 
Data Sources:
HuggingFaceH4/no_robots
Methodology:
fine-tuning on instruction-following data
Model Architecture:
Structured state space model with hardware-aware design
Input Output 
Input Format:
text-based prompts with chat template
Accepted Modalities:
text
Output Format:
generated text responses
LLM NameMamba 2.8B Chat No Robots
Repository 🤗https://huggingface.co/clibrain/mamba-2.8b-chat-no_robots 
Model Size2.8b
Required VRAM5.5 GB
Updated2025-08-21
Maintainerclibrain
Model Files  5.5 GB
Supported Languagesen
Model ArchitectureAutoModel
Licensewtfpl
Tokenizer ClassGPTNeoXTokenizer
Padding Token<|endoftext|>
Vocabulary Size50277

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Note: green Score (e.g. "73.2") means that the model is better than clibrain/mamba-2.8b-chat-no_robots.

Rank the Mamba 2.8B Chat No Robots 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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