Phi 2 Electrical Engineering GPTQ by TheBloke

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Phi 2 Electrical Engineering GPTQ is an open-source language model by TheBloke. Features: 2.8b LLM, VRAM: 1.8GB, Context: 2K, License: other, Quantized, LLM Explorer Score: 0.11.

  4-bit Base model:quantized:stem-ai-m... Base model:stem-ai-mtl/phi-2-e...   Custom code Dataset:garage-baind/open-plat... Dataset:stem-ai-mtl/electrical...   Electrical engineering   En   Fp16   Gptq   Microsoft   Phi   Phi-2   Quantized   Region:us   Safetensors

Phi 2 Electrical Engineering GPTQ Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

Phi 2 Electrical Engineering GPTQ Parameters and Internals

Model Type 
Q&A, code generation
Training Details 
Data Sources:
STEM-AI-mtl/Electrical-engineering, garage-bAInd/Open-Platypus
Methodology:
LoRa PEFT
Hardware Used:
48 Gb A40 Nvidia GPU
LLM NamePhi 2 Electrical Engineering GPTQ
Repository 🤗https://huggingface.co/TheBloke/phi-2-electrical-engineering-GPTQ 
Model NamePhi 2 Electrical Engineering
Model Creatormod
Base Model(s)  Phi 2 Electrical Engineering   STEM-AI-mtl/phi-2-electrical-engineering
Model Size2.8b
Required VRAM1.8 GB
Updated2026-04-27
MaintainerTheBloke
Model Typephi-msft
Model Files  1.8 GB
Supported Languagesen
GPTQ QuantizationYes
Quantization Typefp16|gptq
Model ArchitecturePhiForCausalLM
Licenseother
Context Length2048
Model Max Length2048
Transformers Version4.36.2
Tokenizer ClassCodeGenTokenizer
Vocabulary Size51200
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/phi-2-electrical-engineering-GPTQ.

Rank the Phi 2 Electrical Engineering GPTQ 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 v20260328a