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Qwen2.5 0.5B Instruct Abliterated by ibrahimkettaneh

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  Arxiv:2407.10671   Autotrain compatible Base model:finetune:qwen/qwen2...   Base model:qwen/qwen2.5-0.5b   Chat   Conversational   En   Endpoints compatible   Instruct   Qwen2   Region:us
Model Card on HF 🤗: https://huggingface.co/ibrahimkettaneh/Qwen2.5-0.5B-Instruct-abliterated 

Qwen2.5 0.5B Instruct Abliterated Benchmarks

LLME Score: 0.20282
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
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Qwen2.5 0.5B Instruct Abliterated Parameters and Internals

Model Type 
Causal Language Models
Supported Languages 
en (English)
Training Details 
Methodology:
Pretraining & Post-training
Context Length:
32768
Model Architecture:
Transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings
LLM NameQwen2.5 0.5B Instruct Abliterated
Repository 🤗https://huggingface.co/ibrahimkettaneh/Qwen2.5-0.5B-Instruct-abliterated 
Base Model(s)  Qwen/Qwen2.5-0.5B   Qwen/Qwen2.5-0.5B
Model Size0.5b
Updated2025-04-29
Maintaineribrahimkettaneh
Model Typeqwen2
Instruction-BasedYes
Supported Languagesen
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.43.1
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size151936
Torch Data Typebfloat16
Errorsreplace
Qwen2.5 0.5B Instruct Abliterated (ibrahimkettaneh/Qwen2.5-0.5B-Instruct-abliterated)

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Note: green Score (e.g. "73.2") means that the model is better than ibrahimkettaneh/Qwen2.5-0.5B-Instruct-abliterated.

Rank the Qwen2.5 0.5B Instruct Abliterated 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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Release v20241124
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