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Gemma 2 2B It ThinkLink by MinnieMin

 »  All LLMs  »  MinnieMin  »  Gemma 2 2B It ThinkLink   URL Share it on

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  Autotrain compatible Base model:finetune:google/gem... Base model:google/gemma-2-2b-i...   Code-generation   Coding-education   Conversational   Dataset:raybernard/leetcode   En   Endpoints compatible   Gemma2   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/MinnieMin/gemma-2-2b-it-ThinkLink 

Gemma 2 2B It ThinkLink Benchmarks

LLME Score: 0.22886
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gemma 2 2B It ThinkLink (MinnieMin/gemma-2-2b-it-ThinkLink)

Gemma 2 2B It ThinkLink Parameters and Internals

Model Type 
Causal Language Model
Use Cases 
Primary Use Cases:
Educational purposes, especially for coding test preparation
Limitations:
Direct code generation without understanding the steps may lead to incorrect or misleading results., Not suitable for tasks that require a detailed and immediate answer to general-purpose questions or advanced mathematical computations.
Considerations:
The model encourages self-reflection and thought processes.
Supported Languages 
primary (English)
Training Details 
Methodology:
Low-Rank Adaptation (LoRA)
Training Time:
Approximately 17 hours
Hardware Used:
1 L4 GPU
LLM NameGemma 2 2B It ThinkLink
Repository 🤗https://huggingface.co/MinnieMin/gemma-2-2b-it-ThinkLink 
Base Model(s)  Gemma 2 2B It   google/gemma-2-2b-it
Model Size2b
Required VRAM5.2 GB
Updated2024-12-21
MaintainerMinnieMin
Model Typegemma2
Model Files  2.0 GB: 1-of-3   2.0 GB: 2-of-3   1.2 GB: 3-of-3
Supported Languagesen
Model ArchitectureGemma2ForCausalLM
Licensegemma
Context Length8192
Model Max Length8192
Transformers Version4.44.2
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than MinnieMin/gemma-2-2b-it-ThinkLink.

Rank the Gemma 2 2B It ThinkLink 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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