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Ymail Search Qwen2 0.5B 16bit by kevin36524

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  4bit   Autotrain compatible   Conversational   En   Endpoints compatible   Pytorch   Quantized   Qwen2   Region:us   Sft   Trl   Unsloth
Model Card on HF 🤗: https://huggingface.co/kevin36524/ymail_search_qwen2-0.5B-16bit 

Ymail Search Qwen2 0.5B 16bit Benchmarks

LLME Score: 0.20106
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Ymail Search Qwen2 0.5B 16bit (kevin36524/ymail_search_qwen2-0.5B-16bit)

Ymail Search Qwen2 0.5B 16bit Parameters and Internals

Model Type 
text-generation-inference, transformers, unsloth, qwen2, trl, sft
Additional Notes 
This Qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
LLM NameYmail Search Qwen2 0.5B 16bit
Repository 🤗https://huggingface.co/kevin36524/ymail_search_qwen2-0.5B-16bit 
Base Model(s)  unsloth/Qwen2-0.5b-bnb-4bit   unsloth/Qwen2-0.5b-bnb-4bit
Model Size0.5b
Required VRAM1 GB
Updated2025-01-15
Maintainerkevin36524
Model Typeqwen2
Model Files  1.0 GB
Supported Languagesen
Quantization Type4bit
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length131072
Model Max Length131072
Transformers Version4.41.2
Tokenizer ClassQwen2Tokenizer
Padding Token<|PAD_TOKEN|>
Vocabulary Size151936
Torch Data Typefloat16
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than kevin36524/ymail_search_qwen2-0.5B-16bit.

Rank the Ymail Search Qwen2 0.5B 16bit 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227
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