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Mosaicml Mpt 7B Instruct W4 G128 AWQ by abhinavkulkarni

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Mosaicml Mpt 7B Instruct W4 G128 AWQ is an open-source language model by abhinavkulkarni. Features: 7b LLM, VRAM: 3.8GB, License: cc-by-sa-3.0, Quantized, Instruction-Based, LLM Explorer Score: 0.08.

  Awq   Custom code   Instruct   Mosaicml   Mpt   Pytorch   Quantized   Region:us
Model Card on HF 🤗: https://huggingface.co/abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq 

Mosaicml Mpt 7B Instruct W4 G128 AWQ Benchmarks

LLME Score: 0.07943
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mosaicml Mpt 7B Instruct W4 G128 AWQ (abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq)
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Mosaicml Mpt 7B Instruct W4 G128 AWQ Parameters and Internals

Model Type 
text generation
LLM NameMosaicml Mpt 7B Instruct W4 G128 AWQ
Repository 🤗https://huggingface.co/abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq 
Model Size7b
Required VRAM3.8 GB
Updated2026-05-14
Maintainerabhinavkulkarni
Model Typempt
Instruction-BasedYes
Model Files  3.8 GB
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureMPTForCausalLM
Licensecc-by-sa-3.0
Model Max Length2048
Transformers Version4.33.1
Tokenizer ClassGPTNeoXTokenizer
Vocabulary Size50432
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq.

Rank the Mosaicml Mpt 7B Instruct W4 G128 AWQ 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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