---
datasets:
- emozilla/yarn-train-tokenized-16k-mistral
metrics:
- perplexity
library_name: transformers
---

# Model Card: Nous-Yarn-Mistral-7b-64k

[Preprint (arXiv)](https://arxiv.org/abs/2309.00071)  
[GitHub](https://github.com/jquesnelle/yarn)
![yarn](https://raw.githubusercontent.com/jquesnelle/yarn/mistral/data/proofpile-long-small-mistral.csv.png)

## Model Description

Nous-Yarn-Mistral-7b-64k is a state-of-the-art language model for long context, further pretrained on long context data for 1000 steps using the YaRN extension method.
It is an extension of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and supports a 64k token context window.

To use, pass `trust_remote_code=True` when loading the model, for example

```python
model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Mistral-7b-64k",
  torch_dtype=torch.bfloat16,
  device_map="auto",
  trust_remote_code=True)
```

## Collaborators

 - [bloc97](https://github.com/bloc97): Methods, paper and evals
 - [@theemozilla](https://twitter.com/theemozilla): Methods, paper, model training, and evals
 - [@EnricoShippole](https://twitter.com/EnricoShippole): Model training
 - [honglu2875](https://github.com/honglu2875): Paper and evals

The authors would like to thank LAION AI for their support of compute for this model.
It was trained on the [JUWELS](https://www.fz-juelich.de/en/ias/jsc/systems/supercomputers/juwels) supercomputer.