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Thynk by J-LAB

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Thynk is an open-source language model by J-LAB. Features: 3.1b LLM, VRAM: 6.2GB, Context: 32K, License: apache-2.0, LLM Explorer Score: 0.16.

  Autotrain compatible   Conversational   En   Endpoints compatible   Qwen2   Region:us   Safetensors   Sharded   Tensorflow   Trl   Unsloth
Model Card on HF 🤗: https://huggingface.co/J-LAB/Thynk 

Thynk Benchmarks

LLME Score: 0.15585
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Thynk (J-LAB/Thynk)
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Thynk Parameters and Internals

LLM NameThynk
Repository 🤗https://huggingface.co/J-LAB/Thynk 
Base Model(s)  Thiny_Extra   /Thiny_Extra
Model Size3.1b
Required VRAM6.2 GB
Updated2024-10-15
MaintainerJ-LAB
Model Typeqwen2
Model Files  5.0 GB: 1-of-2   1.2 GB: 2-of-2
Supported Languagesen
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.45.2
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size151936
Torch Data Typebfloat16
Errorsreplace

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Saba Passthrough 2128K / 6.2 GB210
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Augmented 619958b5bf46bea232K / 6.2 GB450
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OpenCognito R232K / 6.2 GB2590
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Bbhqwen32K / 12.3 GB150
Bbhqwen232K / 12.3 GB70
Bbhqwen432K / 12.3 GB130
Bbhqwen532K / 12.3 GB60
Note: green Score (e.g. "73.2") means that the model is better than J-LAB/Thynk.

Rank the Thynk 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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