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Mpt 7B 8K by mosaicml

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  Arxiv:1909.08053   Arxiv:2010.04245   Arxiv:2108.12409   Arxiv:2205.14135   Arxiv:2302.06675   Arxiv:2302.13971   Autotrain compatible   Composer   Custom code   Dataset:allenai/s2orc   Dataset:bigcode/the-stack   Dataset:c4   Dataset:mc4 Dataset:togethercomputer/redpa...   Ext 8k   Llm-foundry   Mosaicml   Mpt   Pytorch   Region:us   Sharded   Streamingdatasets
Model Card on HF 🤗: https://huggingface.co/mosaicml/mpt-7b-8k 

Mpt 7B 8K Benchmarks

ARC: 47.35 vs 96.7 (so35)-51%
HellaSwag: 77.4 vs 95.3 (gpt4)-18.8%
MMLU: 42.58 vs 88.3 (so35)-51.8%
TruthfulQA: 36.65 vs 59 (gpt4)-37.9%
WinoGrande: 71.11 vs 87.5 (gpt4)-18.7%
GSM8K: 8.34 vs 96.4 (so35)-91.3%
LLME Score: 0.14284
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mpt 7B 8K (mosaicml/mpt-7b-8k)
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Mpt 7B 8K Parameters and Internals

Model Type 
decoder-style transformer, LLM, multimodal
Use Cases 
Areas:
research, commercial applications
Applications:
text generation, long-form instruction following, dialogue generation
Primary Use Cases:
finetuning for specific applications
Limitations:
not intended for deployment without finetuning, can produce factually incorrect output
Considerations:
Efforts made to clean pretraining data; however, outputs may still be offensive or biased.
Additional Notes 
This model builds on the MPT-7B with longer sequence handling and significant efficiency improvements.
Supported Languages 
English (proficient)
Training Details 
Data Sources:
mc4, c4, togethercomputer/RedPajama-Data-1T, bigcode/the-stack, allenai/s2orc
Data Volume:
1.5T tokens
Methodology:
MPT-7B-8k uses a modified transformer architecture, optimized for efficient training and inference with ALiBi for handling long inputs.
Context Length:
8192
Training Time:
9.5 days
Hardware Used:
440 A100-40GB GPUs
Model Architecture:
Decoder-only transformer with modifications such as FlashAttention, ALiBi, elimination of positional embeddings.
Safety Evaluation 
Ethical Considerations:
MPT-7B-8k can produce factually incorrect, lewd, biased or offensive outputs. It should not be used for human-facing interactions without further guardrails and user consent.
Responsible Ai Considerations 
Fairness:
Model may have biases inherited from training data.
Transparency:
Pretraining data was openly available, preprocessed to remove unsuitable content.
Accountability:
Responsibility of MosaicML.
Mitigation Strategies:
Guardrails recommended before deployment.
Input Output 
Input Format:
Text sequences, up to 8k tokens
Accepted Modalities:
text
Output Format:
Generated text
Performance Tips:
Utilize optimized implementations like FlashAttention and ensure usage with bfloat16 precision on GPUs.
Release Notes 
Version:
1.0.0
Date:
2023-07-18
Notes:
Initial release of MPT-7B-8k.
LLM NameMpt 7B 8K
Repository 🤗https://huggingface.co/mosaicml/mpt-7b-8k 
Model Size7b
Required VRAM13.3 GB
Updated2025-09-23
Maintainermosaicml
Model Typempt
Model Files  9.9 GB: 1-of-2   3.4 GB: 2-of-2
Context Length8k
Model ArchitectureMPTForCausalLM
Licenseapache-2.0
Model Max Length8192
Transformers Version4.30.2
Tokenizer ClassGPTNeoXTokenizer
Vocabulary Size50432
Torch Data Typebfloat16

Quantized Models of the Mpt 7B 8K

Model
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VRAM
Mpt 7B Q81216 GB

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Note: green Score (e.g. "73.2") means that the model is better than mosaicml/mpt-7b-8k.

Rank the Mpt 7B 8K 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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