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REBEL OpenChat 3.5 by Cornell-AGI

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REBEL OpenChat 3.5 is an open-source language model by Cornell-AGI. Features: LLM, VRAM: 14.4GB, Context: 8K, License: apache-2.0, LLM Explorer Score: 0.13.

  Arxiv:2404.16767 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: 0.13272
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)
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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
Updated2026-04-28
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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Krutrim 2 Instruct1000K / 49.3 GB25836
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Mistral Large Instruct 2407128K / 226.7 GB7270860
Tiny Random MistralForCausalLM128K / 0 GB9051
Winterreise M732K / 14.4 GB00
Frostwind V2.1 M732K / 14.4 GB00
K2S3 V0.132K / 28.7 GB70
MistralLite32K / 14.4 GB10607434
...ydaz Web AI Reasoner BaseModel32K / 14.4 GB01
MistralLite32K / 14.4 GB61777430
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

🆘 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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