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Mpt 30B Instruct GGML by TheBloke

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Mpt 30B Instruct GGML is an open-source language model by TheBloke. Features: 30b LLM, VRAM: 16.9GB, License: cc-by-sa-3.0, Quantized, Instruction-Based, LLM Explorer Score: 0.08.

  Arxiv:2108.12409   Arxiv:2205.14135   Composer   Ggml   Instruct   Llm-foundry   Mosaicml   Mpt   Quantized   Region:us
Model Card on HF 🤗: https://huggingface.co/TheBloke/mpt-30B-instruct-GGML 

Mpt 30B Instruct GGML Benchmarks

LLME Score: 0.0823
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mpt 30B Instruct GGML (TheBloke/mpt-30B-instruct-GGML)
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Mpt 30B Instruct GGML Parameters and Internals

Model Type 
instruction-following
Additional Notes 
The model uses a modified decoder-only transformer architecture, integrating technologies such as FlashAttention and ALiBi for improved performance.
Training Details 
Data Sources:
competition_math, conceptofmind/cot_submix_original/cot_gsm8k, knkarthick/dialogsum, mosaicml/dolly_hhrlhf, duorc, tau/scrolls/qasper, emozilla/quality, scrolls/summ_screen_fd, spider
Data Volume:
2048 tokens during initial training phase, adapted for 8192 tokens
Methodology:
finetuned on instruction following datasets such as Dolly HHRLHF
Context Length:
16384
Training Time:
8 hours
Hardware Used:
72 A100 40GB GPUs
Model Architecture:
Modified decoder-only transformer
Input Output 
Input Format:
Prompt template with instruction and response format
Accepted Modalities:
text
Output Format:
Generated text based on given instruction
LLM NameMpt 30B Instruct GGML
Repository 🤗https://huggingface.co/TheBloke/mpt-30B-instruct-GGML 
Model Size30b
Required VRAM16.9 GB
Updated2026-03-30
MaintainerTheBloke
Model Typempt
Instruction-BasedYes
Model Files  16.9 GB   18.7 GB   20.6 GB   22.5 GB   31.8 GB
GGML QuantizationYes
Quantization Typeggml
Model ArchitectureAutoModel
Licensecc-by-sa-3.0

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/mpt-30B-instruct-GGML.

Rank the Mpt 30B Instruct GGML 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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