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REBEL OpenChat 3.5 by Cornell-AGI

 »  All LLMs  »  Cornell-AGI  »  REBEL OpenChat 3.5   URL Share it on

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  Arxiv:2404.16767   Autotrain compatible Base model:finetune:openchat/o... Base model:openchat/openchat 3...   Conversational   Dataset:berkeley-nest/nectar   En   Endpoints compatible   Mistral   Pytorch   Region:us   Sharded
Model Card on HF 🤗: https://huggingface.co/Cornell-AGI/REBEL-OpenChat-3.5 

REBEL OpenChat 3.5 Benchmarks

LLME Score: 0.18893
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
REBEL OpenChat 3.5 (Cornell-AGI/REBEL-OpenChat-3.5)

REBEL OpenChat 3.5 Parameters and Internals

Additional Notes 
The model's training code is publicly available.
Supported Languages 
en (Proficient)
Training Details 
Data Sources:
berkeley-nest/Nectar
Methodology:
Reinforcement Learning via Regressing Relative Rewards (REBEL) using Starling-RM-7B-alpha as the reward model.
LLM NameREBEL OpenChat 3.5
Repository 🤗https://huggingface.co/Cornell-AGI/REBEL-OpenChat-3.5 
Base Model(s)  openchat/openchat_3.5   openchat/openchat_3.5
Required VRAM14.4 GB
Updated2025-02-22
MaintainerCornell-AGI
Model Typemistral
Model Files  4.9 GB: 1-of-3   5.0 GB: 2-of-3   4.5 GB: 3-of-3
Supported Languagesen
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length8192
Model Max Length8192
Transformers Version4.40.1
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size32002
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than Cornell-AGI/REBEL-OpenChat-3.5.

Rank the REBEL OpenChat 3.5 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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