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Microsoft Phi 2 Trained Abliterated QA 1960 All Modules Merged by M1LL1X

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Microsoft Phi 2 Trained Abliterated QA 1960 All Modules Merged is an open-source language model by M1LL1X. Features: 2.8b LLM, VRAM: 5.6GB, Context: 2K, LLM Explorer Score: 0.16.

  Arxiv:1910.09700   Conversational   Endpoints compatible   Phi   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/M1LL1X/microsoft-phi-2-trained-abliterated-QA-1960-all-modules-merged 

Microsoft Phi 2 Trained Abliterated QA 1960 All Modules Merged Benchmarks

LLME Score: 0.16235
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Microsoft Phi 2 Trained Abliterated QA 1960 All Modules Merged (M1LL1X/microsoft-phi-2-trained-abliterated-QA-1960-all-modules-merged)
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Microsoft Phi 2 Trained Abliterated QA 1960 All Modules Merged Parameters and Internals

LLM NameMicrosoft Phi 2 Trained Abliterated QA 1960 All Modules Merged
Repository 🤗https://huggingface.co/M1LL1X/microsoft-phi-2-trained-abliterated-QA-1960-all-modules-merged 
Model Size2.8b
Required VRAM5.6 GB
Updated2026-05-05
MaintainerM1LL1X
Model Typephi
Model Files  5.0 GB: 1-of-2   0.6 GB: 2-of-2
Model ArchitecturePhiForCausalLM
Context Length2048
Model Max Length2048
Transformers Version4.46.2
Tokenizer ClassCodeGenTokenizer
Padding Token<|im_end|>
Vocabulary Size50297
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than M1LL1X/microsoft-phi-2-trained-abliterated-QA-1960-all-modules-merged.

Rank the Microsoft Phi 2 Trained Abliterated QA 1960 All Modules Merged 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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