adamrb/mpt-30b-chat-safetensors

MPT-30B-Chat (SafeTensors Format)

This is a SafeTensors conversion of the original MPT-30B-Chat model from MosaicML. The model weights have been converted from PyTorch .bin format to the more efficient and secure SafeTensors format while maintaining full compatibility with the original model.

MPT-30B-Chat is a chatbot-like model for dialogue generation. It was built by finetuning MPT-30B on the ShareGPT-Vicuna, Camel-AI, GPTeacher, Guanaco, Baize and some generated datasets.

• License: CC-By-NC-SA-4.0 (non-commercial use only)

This model was trained by MosaicML and follows a modified decoder-only transformer architecture.

Key Improvements

• SafeTensors Format: Faster loading, better security, and more reliable serialization
• Reduced Memory Usage: More efficient memory usage during model loading
• Better Compatibility: Works seamlessly with modern ML frameworks and tools
• Maintained Quality: Identical performance to the original model

Model Date

June 22, 2023 (Original), Converted: July 2025

Model License

CC-By-NC-SA-4.0 (non-commercial use only)

Documentation

• Blog post: Raising the bar for open-source foundation models
• Codebase (mosaicml/llm-foundry repo)
• Questions: Feel free to contact us via the MosaicML Community Slack!

How to Use

This model is best used with the MosaicML llm-foundry repository for training and finetuning.

import transformers

model = transformers.AutoModelForCausalLM.from_pretrained(
    'adamrb/mpt-30b-chat-safetensors',
    trust_remote_code=True
)

Note: This model requires that trust_remote_code=True be passed to the from_pretrained method. This is because we use a custom MPT model architecture that is not yet part of the Hugging Face transformers package.

MPT includes options for many training efficiency features such as FlashAttention, ALiBi, QK LayerNorm, and more.

To use the optimized triton implementation of FlashAttention, you can load the model on GPU (cuda:0) with attn_impl='triton' and with bfloat16 precision:

import torch
import transformers

name = 'adamrb/mpt-30b-chat-safetensors'
config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
config.attn_config['attn_impl'] = 'triton'  # change this to use triton-based FlashAttention
config.init_device = 'cuda:0'  # For fast initialization directly on GPU!

model = transformers.AutoModelForCausalLM.from_pretrained(
    name,
    config=config,
    torch_dtype=torch.bfloat16,  # Load model weights in bfloat16
    trust_remote_code=True
)

The model was trained initially with a sequence length of 2048 with an additional pretraining stage for sequence length adapation up to 8192. However, ALiBi enables users to increase the maximum sequence length even further during finetuning and/or inference. For example:

import transformers

name = 'adamrb/mpt-30b-chat-safetensors'
config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
config.max_seq_len = 16384  # (input + output) tokens can now be up to 16384

model = transformers.AutoModelForCausalLM.from_pretrained(
    name,
    config=config,
    trust_remote_code=True
)

This model was trained with the MPT-30B tokenizer which is based on the EleutherAI/gpt-neox-20b tokenizer and includes additional padding and eos tokens.

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('adamrb/mpt-30b-chat-safetensors')

The model can then be used, for example, within a text-generation pipeline. Note: when running Torch modules in lower precision, it is best practice to use the torch.autocast context manager.

from transformers import pipeline

with torch.autocast('cuda', dtype=torch.bfloat16):
    inputs = tokenizer('Here is a recipe for vegan banana bread:\n', return_tensors="pt").to('cuda')
    outputs = model.generate(**inputs, max_new_tokens=100)
    print(tokenizer.batch_decode(outputs, skip_special_tokens=True))

    # or using the HF pipeline
    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))

Model Description

The architecture is a modification of a standard decoder-only transformer.

The model has been modified from a standard transformer in the following ways:

• It uses FlashAttention
• It uses ALiBi (Attention with Linear Biases) and does not use positional embeddings
• It does not use biases

Hyperparameter	Value
n_parameters	29.95B
n_layers	48
n_heads	64
d_model	7168
vocab size	50432
sequence length	8192

Data Mix

The model was trained on the following data mix:

Data Source	Number of Tokens in Source	Proportion
Airoboros/GPT4-1.2	26.4M	1.71%
Baize	55.0M	3.57%
Camel	301M	19.54%
GPTeacher	7.56M	0.49%
Guanaco	15.6M	1.02%
LongCoversations	18.4M	1.19%
ShareGPT	821M	53.24%
WizardLM	297M	19.23%

"LongConversations" is a GPT3.5/4-generated dataset, details of which will be released at a later date.

Training Configuration

This model was trained on 64 H100s for about 7.6 hours using the MosaicML Platform. The model was trained with sharded data parallelism using FSDP and used the AdamW optimizer.

Limitations and Biases

The following language is modified from EleutherAI's GPT-NeoX-20B

MPT-30B-Chat can produce factually incorrect output, and should not be relied on to produce factually accurate information. MPT-30B-Chat was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.

Acknowledgements

This model was finetuned by Sam Havens and the MosaicML NLP team. SafeTensors conversion by adamrb.

Disclaimer

The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.

Citation

Please cite this model using the following format:

@online{MosaicML2023Introducing,
    author = {MosaicML NLP Team},
    title = {Introducing MPT-30B: Raising the bar for open-source foundation models},
    year = {2023},
    url = {www.mosaicml.com/blog/mpt-30b},
    note = {Accessed: 2023-06-22},
    urldate = {2023-06-22}
}