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Qwen2 Simple Arguments by cris177

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  4bit   Autotrain compatible   Conversational   En   Endpoints compatible   Gguf   Model-index   Quantized   Qwen2   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/cris177/Qwen2-Simple-Arguments 

Qwen2 Simple Arguments Benchmarks

LLME Score: 0.20372
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Qwen2 Simple Arguments (cris177/Qwen2-Simple-Arguments)

Qwen2 Simple Arguments Parameters and Internals

Model Type 
LLM
Supported Languages 
English (NLP)
Training Details 
Data Sources:
synthetic data based on various types of logical arguments
Data Volume:
50k arguments created for training and 100 for testing
Methodology:
The data was converted to the Alpaca chat format before feeding it into the model. We used unsloth for memory reduced sped up training. We trained for one epoch.
Training Time:
2.5 hours
Hardware Used:
Less than 2.5 GB of VRAM were used for training
LLM NameQwen2 Simple Arguments
Repository 🤗https://huggingface.co/cris177/Qwen2-Simple-Arguments 
Model Size0.5b
Required VRAM0.4 GB
Updated2025-02-22
Maintainercris177
Model Typeqwen2
Model Files  1.0 GB   0.4 GB   1.0 GB
Supported Languagesen
GGUF QuantizationYes
Quantization Type4bit|gguf
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length131072
Model Max Length131072
Transformers Version4.42.0
Tokenizer ClassQwen2Tokenizer
Padding Token<|PAD_TOKEN|>
Vocabulary Size151936
Torch Data Typebfloat16
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than cris177/Qwen2-Simple-Arguments.

Rank the Qwen2 Simple Arguments 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  
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Release v20241227
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