⚠️ ARCHIVED / LEGACY MODEL NOTICE
This repository is part of a legacy collection quantized around 2023. To manage storage quotas and maintain active community projects, some rarely used quantization formats (e.g., Q2_K, Q3_K, Q4_1, Q5_1) have been permanently removed.
Only the most popular and stable formats (Q4_0, Q4_K_M, Q5_K_M, Q6_K, and Q8_0) remain available.
💡 Looking for something modern?
If you are starting a new project, we highly recommend using newer architectures (like Llama 3, Mistral, or Qwen) provided by official maintainers or active community members (e.g., Bartowski, TheBloke legacy files, or official organization handles).
⚠️ This repository is no longer actively maintained. Existing files are provided "as is" for archival and legacy hardware purposes.
MPT-7b and MPT-30B are part of the family of Mosaic Pretrained Transformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference.
About GGUF format
gguf is the current file format used by the ggml library.
A growing list of Software is using it and can therefore use this model.
The core project making use of the ggml library is the llama.cpp project by Georgi Gerganov
Quantization variants
There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option for you:
Legacy quants
Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are legacy quantization types.
Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants.
Note:
Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not real K-quants. More details can be found in affected model descriptions.
(This mainly refers to Falcon 7b and Starcoder models)
K-quants
K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load.
So, if possible, use K-quants.
With a Q6_K, you'll likely find it challenging to discern a quality difference from the original model - ask your model two times the same question and you may encounter bigger quality differences.
A Quoll (pronounced “cool”) is one of Australia’s native carnivorous marsupial mammals, which are also known as macropods or wallabies in other parts around Asia and South America
How to Use
Note: This model requires that trust_remote_code=True be passed to the from_pretrained method. This is because we use a custom model architecture that is not yet part of the transformers package.
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:
python
1import torch
2import transformers
34name ='mosaicml/mpt-7b-instruct'56config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)7config.attn_config['attn_impl']='triton'8config.init_device ='cuda:0'# For fast initialization directly on GPU!910model = transformers.AutoModelForCausalLM.from_pretrained(11 name,12 config=config,13 torch_dtype=torch.bfloat16,# Load model weights in bfloat1614 trust_remote_code=True15)
Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example:
python
1import transformers
23name ='mosaicml/mpt-7b-instruct'45config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)6config.max_seq_len =4096# (input + output) tokens can now be up to 409678model = transformers.AutoModelForCausalLM.from_pretrained(9 name,10 config=config,11 trust_remote_code=True12)
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.
python
1from transformers import pipeline
23pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0')45with torch.autocast('cuda', dtype=torch.bfloat16):6print(7 pipe('Here is a recipe for vegan banana bread:\n',8 max_new_tokens=100,9 do_sample=True,10 use_cache=True))
Formatting
This model was trained on data formatted in the dolly-15k format:
python
1INSTRUCTION_KEY ="### Instruction:"2RESPONSE_KEY ="### Response:"3INTRO_BLURB ="Below is an instruction that describes a task. Write a response that appropriately completes the request."4PROMPT_FOR_GENERATION_FORMAT ="""{intro}
5{instruction_key}
6{instruction}
7{response_key}
8""".format(9 intro=INTRO_BLURB,10 instruction_key=INSTRUCTION_KEY,11 instruction="{instruction}",12 response_key=RESPONSE_KEY,13)1415example ="James decides to run 3 sprints 3 times a week. He runs 60 meters each sprint. How many total meters does he run a week? Explain before answering."16fmt_ex = PROMPT_FOR_GENERATION_FORMAT.format(instruction=example)
In the above example, fmt_ex is ready to be tokenized and sent through the model.
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:
This model was trained on 8 A100-40GBs for about 2.3 hours using the MosaicML Platform.
The model was trained with sharded data parallelism using FSDP and used the AdamW optimizer.
MPT-7B-Instruct can produce factually incorrect output, and should not be relied on to produce factually accurate information.
MPT-7B-Instruct 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
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 cosult 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-7B: A New Standard for Open-Source, Commercially Usable LLMs},
year = {2023},
url = {www.mosaicml.com/blog/mpt-7b},
note = {Accessed: 2023-03-28}, % change this date
urldate = {2023-03-28} % change this date
}
End of original Model File
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Coming Soon: I'm in the process of launching a sponsorship/crowdfunding campaign for my work. I'm evaluating Kickstarter, Patreon, or the new GitHub Sponsors platform, and I am hoping for some support and contribution to the continued availability of these kind of models. Your support will enable me to provide even more valuable resources and maintain the models you rely on. Your patience and ongoing support are greatly appreciated as I work to make this page an even more valuable resource for the community.
