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.
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-8k'56config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)7config.attn_config['attn_impl']='triton'# change this to use triton-based FlashAttention8config.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)
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:
python
1import transformers
23name ='mosaicml/mpt-7b-instruct-8k'45config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)6config.max_seq_len =16384# (input + output) tokens can now be up to 1638478model = transformers.AutoModelForCausalLM.from_pretrained(9 name,10 config=config,11 trust_remote_code=True12)
This model was trained with the MPT-7B-chat tokenizer which is based on the EleutherAI/gpt-neox-20b tokenizer and includes additional ChatML tokens.
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
23with torch.autocast('cuda', dtype=torch.bfloat16):4 inputs = tokenizer('Here is a recipe for vegan banana bread:\n', return_tensors="pt").to('cuda')5 outputs = model.generate(**inputs, max_new_tokens=100)6print(tokenizer.batch_decode(outputs, skip_special_tokens=True))78# or using the HF pipeline9pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0')10with torch.autocast('cuda', dtype=torch.bfloat16):11print(12 pipe('Here is a recipe for vegan banana bread:\n',13 max_new_tokens=100,14 do_sample=True,15 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:
This model was trained on 8 80GB A100s for about 6.3 hours using the MosaicML Platform.
The model was trained with sharded data parallelism using FSDP and used the AdamW optimizer.
MPT-7B-Instruct-8k can produce factually incorrect output, and should not be relied on to produce factually accurate information.
MPT-7B-Instruct-8k 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 the MosaicML NLP team.
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.
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}
}
End of original Model File
Please consider to support my work
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.
