⚠️ 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⚠️ This repository is no longer actively maintained.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.
Original Model Card:
MPT-7B-8k
MPT-7B-8k is a decoder-style transformer pretrained starting from MPT-7B, but updating the sequence length to 8k and training for an additional 500B tokens, resulting in a total of 1.5T tokens of text and code.
This model was trained by MosaicML.
MPT-7B-8k is part of the family of Mosaic Pretrained Transformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference.
These architectural changes include performance-optimized layer implementations and the elimination of context length limits by replacing
positional embeddings with Attention with Linear Biases (ALiBi).
Thanks to these modifications, MPT models can be trained with high throughput efficiency and stable convergence.
MPT models can also be served efficiently with both standard HuggingFace pipelines and NVIDIA's FasterTransformer.
This model uses the MosaicML LLM codebase, which can be found in the llm-foundry repository. It was trained by MosaicML’s NLP team on the MosaicML platform for LLM pretraining, finetuning, and inference.
Prepared to handle long inputs thanks to ALiBi. With ALiBi, the model can extrapolate beyond the 8k training sequence length to up to 10k, and with a few million tokens it can be finetuned to extrapolate much further.
MPT-7B-8k-Instruct: a model for long-form instruction following (especially summarization and question-answering).
Built by finetuning MPT-7B-8k on several carefully curated datasets.
License: CC-BY-SA-3.0
MPT-7B-8k-Chat: a chatbot-like model for dialogue generation.
Built by finetuning MPT-7B-8k on approximately 1.5B tokens of chat data.
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-8k'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-8k'45config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)6config.max_seq_len =10000# (input + output) tokens can now be up to 1000078model = transformers.AutoModelForCausalLM.from_pretrained(9 name,10 config=config,11 trust_remote_code=True12)
This model was trained with the MPT-7B-8k tokenizer which is identical to the EleutherAI/gpt-neox-20b tokenizer.
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:
Data was formatted using the MosaicML StreamingDataset library to host our data in object storage and efficiently stream it to our compute cluster during training.
StreamingDataset obviates the need to download the whole dataset before starting training, and allows instant resumption of training from any point in the dataset.
Data Mix
The model was trained for ___T tokens. First it was trained for 1T tokens (with batch size 1760 and sequence length 2048) on the following data mix:
Data Mix for Original 1T Tokens Used to Train MPT-7B
Data Source
Number of Tokens in Source
Proportion
Effective Number of Tokens
Epochs
mC4 3.1.0 - English
417.99 B
0.33
330 B
0.14
C4 - English - SemDedup 80%
100.42 B
0.299
299 B
2.98
RedPajama - CommonCrawl
878.45 B
0.1
100 B
0.11
The Stack - Selected Languages
463.78 B
0.1
100 B
0.22
RedPajama - Wikipedia - En
4.87 B
0.04
40 B
8.21
The Stack - Markdown
107.07 B
0.035
35 B
0.33
S2ORC
48.85 B
0.033
33 B
0.68
RedPajama - Books
26.02 B
0.03
30B
1.15
RedPajama - arXiv
28.10 B
0.019
19 B
0.68
RedPajama - StackExchange
20.54 B
0.014
14 B
0.68
Data Mix for Additional 500B Tokens Used to Further Train MPT-7B-8k
We took 80B tokens from document samples that were longer than 4096 tokens, and 120B tokens with varying document sample lengths that matched the "baseline" length distribution for a total of 200B tokens in a single dataset.
We then trained MPT-7B for 500B tokens with a maximum sequence length of 8192, resulting in MPT-7B-8k. Since we trained for 500B tokens using 200B tokens, nearly every subset was trained on for exactly 2.5 epochs.
Sequence Length Distribution
Number of Tokens in Source (Billion)
Proportion
Effective Number of Tokens (Billion)
Epochs
mC4 3.1.0 - English (200+ words) - Baseline
33.60
16.80%
84.00
2.50
mC4 3.1.0 - English (200+ words) - ≥4096 tokens
23.04
11.52%
57.60
2.50
c4 - English - SemDedup 80% - Baseline
30.12
15.06%
75.30
2.50
c4 - English - SemDedup 80% - ≥4096 tokens
0.92
0.46%
2.30
2.50
RedPajama - CommonCrawl - Baseline
8.52
4.26%
21.30
2.50
RedPajama - CommonCrawl - ≥4096 tokens
12.80
6.40%
32.00
2.50
The Stack - Selected Languages - Baseline
30.00
15.00%
75.00
2.50
The Stack - Selected Languages - ≥4096 tokens
10.00
5.00%
25.00
2.50
RedPajama - Wikipedia - Baseline
3.60
1.80%
9.00
2.50
RedPajama - Wikipedia - ≥4096 tokens
1.04
0.52%
2.60
2.50
The Stack - Markdown - Baseline
4.50
2.25%
11.25
2.50
The Stack - Markdown - ≥4096 tokens
8.00
4.00%
20.00
2.50
Semantic Scholar ORC - Baseline
3.30
1.65%
8.25
2.50
Semantic Scholar ORC - ≥4096 tokens
8.00
4.00%
20.00
2.50
RedPajama - Books - Baseline
3.00
1.50%
7.50
2.50
RedPajama - Books - ≥4096 tokens
8.00
4.00%
20.00
2.50
RedPajama - arXiv - Baseline
1.92
0.96%
4.80
2.50
RedPajama - arXiv - ≥4096 tokens
5.40
2.70%
13.50
2.50
RedPajama - StackExchange - Baseline
1.44
0.72%
3.60
2.50
RedPajama - StackExchange - ≥4096 tokens
1.52
1.40%
7.00
4.60
N Training Tokens
200
100.00%
2.5 epochs * 200B = 500B tokens
Samples for each batch were selected from one of the datasets with the probability specified above.
The examples were shuffled within each dataset, and each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length.
The data was tokenized using the EleutherAI/gpt-neox-20b tokenizer. This BPE tokenizer has a number of desirable characteristics,
most of which are relevant for tokenizing code:
(1) It was trained on a diverse mix of data that includes code (The Pile)
(2) It applies consistent space delimitation, unlike the GPT2 tokenizer which tokenizes inconsistently depending on the presence of prefix spaces
(3) It contains tokens for repeated space characters, which allows superior compression of text with large amounts of repeated space characters.
The model vocabulary size of 50432 was set to be a multiple of 128 (as in MEGATRON-LM), model flop utilization (MFU) increased by up to four percentage points.
Training Configuration
This model was trained on 440 A100-40GBs for about 9.5 days using the MosaicML Platform.
The model was trained with sharded data parallelism using FSDP and used the LION optimizer.
MPT-7B-8k is not intended for deployment without finetuning.
It should not be used for human-facing interactions without further guardrails and user consent.
MPT-7B-8k can produce factually incorrect output, and should not be relied on to produce factually accurate information.
MPT-7B-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.
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,
ly 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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