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Thinik Orpo V2.0 by J-LAB

 »  All LLMs  »  J-LAB  »  Thinik Orpo V2.0   URL Share it on

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  4bit   Autotrain compatible   Conversational   En   Endpoints compatible   Orpo   Quantized   Qwen2   Region:us   Safetensors   Sharded   Tensorflow   Trl   Unsloth
Model Card on HF 🤗: https://huggingface.co/J-LAB/Thinik_Orpo_v2.0 

Thinik Orpo V2.0 Benchmarks

LLME Score: 0.2359
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Thinik Orpo V2.0 (J-LAB/Thinik_Orpo_v2.0)

Thinik Orpo V2.0 Parameters and Internals

Model Type 
text-generation-inference, transformers
Additional Notes 
This qwen2 model was trained 2x faster using Unsloth and Huggingface's TRL library.
Supported Languages 
en (fluent)
Training Details 
Methodology:
Trained 2x faster with Unsloth and Huggingface's TRL library.
LLM NameThinik Orpo V2.0
Repository 🤗https://huggingface.co/J-LAB/Thinik_Orpo_v2.0 
Base Model(s)  unsloth/qwen2.5-3b-bnb-4bit   unsloth/qwen2.5-3b-bnb-4bit
Model Size3b
Required VRAM6.2 GB
Updated2024-12-22
MaintainerJ-LAB
Model Typeqwen2
Model Files  5.0 GB: 1-of-2   1.2 GB: 2-of-2
Supported Languagesen
Quantization Type4bit
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.44.2
Tokenizer ClassQwen2Tokenizer
Padding Token<|PAD_TOKEN|>
Vocabulary Size151936
Torch Data Typebfloat16

Best Alternatives to Thinik Orpo V2.0

Best Alternatives
Context / RAM
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Likes
QwQ LCoT 3B Instruct32K / 6.2 GB1346
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Saba1.5 Pro 3B128K / 5.8 GB671
Qwen2.5 3B Instruct32K / 6.2 GB210716116
Qwen2.5 3B32K / 6.2 GB10147243
Calme 3.3 Instruct 3B32K / 11 GB88282
Qwen2.5 Coder 3B Instruct32K / 6.2 GB6776422
Calme 3.2 Instruct 3B32K / 11 GB28880
Calme 3.1 Baguette 3B32K / 6.2 GB28241
Note: green Score (e.g. "73.2") means that the model is better than J-LAB/Thinik_Orpo_v2.0.

Rank the Thinik Orpo V2.0 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241217
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