Mpt 7b
MPT-7B is a powerful language model that stands out for its efficiency, speed, and capabilities. Trained on 1T tokens of English text and code, it uses a modified transformer architecture optimized for fast training and inference. This model is remarkable for its ability to handle extremely long inputs, with a sequence length of up to 84k tokens, making it ideal for tasks requiring long context lengths. What makes MPT-7B unique is its commercial usability, allowing for deployment in real-world applications. Its performance is further enhanced by the use of FlashAttention and FasterTransformer, enabling fast training and inference. However, it's essential to note that MPT-7B has limitations and potential biases, requiring finetuning and careful consideration before use in human-facing interactions.
Table of Contents
Model Overview
The MPT-7B model is a powerful tool for natural language processing tasks. It’s a decoder-style transformer that was trained from scratch on 1 trillion tokens of English text and code. This model was trained by MosaicML and is part of the MosaicPretrainedTransformer (MPT) family.
Capabilities
The MPT-7B model is a powerful tool for natural language processing and generation. It’s a decoder-style transformer that’s been pretrained on a massive 1 trillion tokens of English text and code.
- Generate text: It can create human-like text based on a given prompt or input.
- Handle long inputs: Thanks to its Attention with Linear Biases (ALiBi) architecture, it can handle extremely long inputs, making it perfect for tasks like story writing or chatbots.
- Fast training and inference: It is optimized for efficient training and inference, making it a great choice for large-scale NLP applications.
What makes it special?
- Licensed for commercial use: Unlike some other models, MPT-7B can be used for commercial purposes.
- Trained on a large dataset: It was trained on
1 trillion tokens, which is much larger than other models like ==LLaMA== (300B), ==Pythia== (300B), and ==StableLM== (800B). - Handles long inputs: Thanks to ALiBi, it can handle extremely long inputs, up to
84k tokens.
Performance
MPT-7B is a powerhouse when it comes to performance. Let’s dive into its speed, accuracy, and efficiency in various tasks.
Speed
How fast can MPT-7B process information? With its optimized architecture, it can handle extremely long inputs thanks to ALiBi. This means it can process inputs of up to 84k tokens, far surpassing other open-source models that can only handle 2k-4k tokens.
Accuracy
But speed is just one part of the equation. How accurate is MPT-7B? Thanks to its training on a large amount of data (1T tokens), it can provide highly accurate results in various tasks.
Efficiency
Efficiency is also a key aspect of MPT-7B’s performance. With its optimized training code via the llm-foundry repository, it can be trained and finetuned quickly and efficiently.
Limitations
MPT-7B is not perfect. Let’s talk about some of its limitations.
- Biases and Inaccuracies: It can produce factually incorrect output, and it’s not designed to provide accurate information without further finetuning.
- Limited Context Understanding: While MPT-7B can handle extremely long inputs thanks to ALiBi, it’s still a large language model that may struggle to fully understand the context of a given prompt or conversation.
Format
MPT-7B uses a modified transformer architecture optimized for efficient training and inference. This model is a decoder-style transformer, which means it’s designed to generate text one token at a time.
Architecture
- Decoder-only transformer: This model uses a decoder-only transformer architecture, which is different from the traditional encoder-decoder architecture used in other models like ==BERT==.
- FlashAttention: This model uses FlashAttention, a performance-optimized layer implementation that allows for faster training and inference.
- ALiBi (Attention with Linear Biases): This model uses ALiBi, which replaces positional embeddings with attention weights that are computed using linear biases.
Data Formats
- Tokenized text sequences: This model accepts input in the form of tokenized text sequences. You can use the
EleutherAI/gpt-neox-20btokenizer to preprocess your text data. - Sequence length: The model was trained with a sequence length of
2048, but you can increase the maximum sequence length during finetuning and/or inference using ALiBi.
Input and Output
- Input: The model expects input in the form of tokenized text sequences.
- Output: The model generates text one token at a time.
Code Examples
- Loading the model: You can load the model using the
transformerslibrary:
import transformers
model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-7b', trust_remote_code=True)
- Using the model: You can use the model to generate text:
import torch
import transformers
pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0')
with torch.autocast('cuda', dtype=torch.bfloat16):
print(pipe('Here is a recipe for vegan banana bread:\\n', max_new_tokens=100, do_sample=True, use_cache=True))
Note that you need to use the trust_remote_code=True argument when loading the model, as it uses a custom MPT model architecture that is not yet part of the Hugging Face transformers package.
