Update README.md (add a link to Mixtral)

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	---
	language:
	- en
	- ja
	library_name: transformers
	pipeline_tag: text-generation
	tag: moe
	license: apache-2.0
	---

	# Swallow-MX-8x7b-NVE-v0.1

	Our Swallow-MX-8x7b-NVE-v0.1 model has undergone continuous pre-training from the [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1), primarily with the addition of Japanese language data.

	![logo](./logo.png)

	## Model Details

	* Model type: Please refer to [Mixtral technical report](https://arxiv.org/abs/2401.04088) for details on the model architecture.
	* Language(s): Japanese English
	* Tokenizer: This model utilizes the same tokenizer as employed by Mixtral-8x7B-Instruct-v0.1.
	* Contact: swallow[at]nlp.c.titech.ac.jp

	## Base Model Performance

	### Japanese version

	\|Model\|Size\|JCommonsenseQA\|JEMHopQA\|NIILC\|JSQuAD\|XL-Sum\|MGSM\|WMT20-en-ja\|WMT20-ja-en\|
	\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|
	\| \| \|4-shot\|4-shot\|4-shot\|4-shot\|1-shot\|4-shot\|4-shot\|4-shot\|
	\| Llama 2 \| 7B \| 0.3852 \| 0.4240 \| 0.3410 \| 0.7917 \| 0.1905 \| 0.0760 \| 0.1783 \| 0.1738 \|
	\| Swallow \| 7B \| 0.4808 \| 0.5078 \| 0.5968 \| 0.8573 \| 0.1830 \| 0.1240 \| 0.2510 \| 0.1511 \|
	\| Swallow-Plus \| 7B \| 0.5478 \| 0.5493 \| 0.6030 \| 0.8544 \| 0.1806 \| 0.1360 \| 0.2568 \| 0.1441 \|
	\| Swallow-NVE \| 7B \| 0.5433 \| 0.5425 \| 0.5729 \| 0.8684 \| 0.2117 \| 0.1200 \| 0.2405 \| 0.1512 \|
	\| Mistral-7B-v0.1 \| 7B \| 0.7301 \| 0.4245 \| 0.2722 \| 0.8563 \| 0.2006 \| 0.1760 \| 0.1405 \| 0.1733 \|
	\|Swallow-MS-7b-v0.1\| 7B \| 0.8570 \| 0.4915 \| 0.5519 \| 0.8802 \| 0.1988 \| 0.2240 \| 0.2494 \| 0.1667 \|
	\| Llama 2 \| 13B \| 0.6997 \| 0.4415 \| 0.4170 \| 0.8533 \| 0.2139 \| 0.1320 \| 0.2146 \| 0.1982 \|
	\| Swallow \| 13B \| 0.7837 \| 0.5063 \| 0.6398 \| 0.9005 \| 0.2168 \| 0.2040 \| 0.2720 \| 0.1771 \|
	\| Swallow-NVE \| 13B \| 0.7712 \| 0.5438 \| 0.6351 \| 0.9030 \| 0.2294 \| 0.2120 \| 0.2735 \| 0.1817 \|
	\| Llama 2 \| 70B \| 0.8686 \| 0.4656 \| 0.5256 \| 0.9080 \| 0.2361 \| 0.3560 \| 0.2643 \| 0.2398 \|
	\| Swallow \| 70B \| 0.9348 \| 0.6290 \| 0.6960 \| 0.9176 \| 0.2266 \| 0.4840 \| 0.3043 \| 0.2298 \|
	\| Swallow-NVE \| 70B \| 0.9410 \| 0.5759 \| 0.7024 \| 0.9254 \| 0.2758 \| 0.4720 \| 0.3042 \| 0.2322 \|
	\|Mixtral-8x7B-v0.1\|8x7B\|0.8347\|0.5335\|0.3549\|0.8847\|0.2192\|0.3120\|0.1970\|0.1987\|
	\|Swallow-MX-8x7b-NVE-v0.1\|8x7B\|0.9258\|0.5843\|0.5687\|0.9148\|0.2589\|0.4360\|0.2705\|0.2074\|

	### English version

	\|Model\|Size\|OpenBookQA\|TriviaQA\|HellaSwag\|SQuAD2.0\|XWINO\|GSM8K\|
	\|---\|---\|---\|---\|---\|---\|---\|---\|
	\| \| \|8-shot\|8-shot\|8-shot\|8-shot\|8-shot\|8-shot\|
	\| Llama 2 \| 7B \| 0.3580 \| 0.6265 \| 0.5860 \| 0.3207 \| 0.9049 \| 0.1410 \|
	\| Swallow \| 7B \| 0.3180 \| 0.4836 \| 0.5308 \| 0.3125 \| 0.8817 \| 0.1130 \|
	\| Swallow-Plus \| 7B \| 0.3280 \| 0.4558 \| 0.5259 \| 0.3134 \| 0.8929 \| 0.1061 \|
	\| Swallow-NVE \| 7B \| 0.3180 \| 0.5079 \| 0.5329 \| 0.2919 \| 0.8817 \| 0.0986 \|
	\| Mistral-7B-v0.1 \| 7B \| 0.3660 \| 0.7050 \| 0.6264 \| 0.3799 \| 0.9157 \| 0.3533 \| 0.3440 \| 0.5976 \| 0.5810 \| 0.3364 \| 0.9037 \| 0.2623 \|
	\|Swallow-MS-7b-v0.1\| 7B \| 0.3440 \| 0.5976 \| 0.5810 \| 0.3364 \| 0.9037 \| 0.2623 \|
	\| Llama 2 \| 13B \| 0.3760 \| 0.7255 \| 0.6148 \| 0.3681 \| 0.9140 \| 0.2403 \|
	\| Swallow \| 13B \| 0.3500 \| 0.5852 \| 0.5660 \| 0.3406 \| 0.9075 \| 0.2039 \|
	\| Swallow-NVE \| 13B \| 0.3460 \| 0.6025 \| 0.5700 \| 0.3478 \| 0.9006 \| 0.1751 \|
	\| Llama 2 \| 70B \| 0.4280 \| 0.8239 \| 0.6742 \| 0.3770 \| 0.9290 \| 0.5284 \|
	\| Swallow \| 70B \| 0.4220 \| 0.7756 \| 0.6458 \| 0.3745 \| 0.9204 \| 0.4867 \|
	\| Swallow-NVE \| 70B \| 0.4240 \| 0.7817 \| 0.6439 \| 0.3451 \| 0.9256 \| 0.4943 \|
	\|Mixtral-8x7B-v0.1\|8x7B\|0.3960\|0.7989\|0.6678\|0.3842\|0.9204\|0.5747\|
	\|Swallow-MX-8x7b-NVE-v0.1\|8x7B\|0.3740\|0.7847\|0.6520\|0.3801\|0.9170\|0.5694\|

	Please note that Swallow-MX-8x7b-NVE-v0.1 is not derived from Mixtral-8x7B-v0.1, but rather underwent continued pre-training from Mixtral-8x7B-Instruct-v0.1.

	## Usage

	First install additional dependencies in [requirements.txt](./requirements.txt):

	```sh
	pip install -r requirements.txt
	```

	### Use the base model

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	model_name = "tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1"
	tokenizer = AutoTokenizer.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")

	model = AutoModelForCausalLM.from_pretrained(model_name)
	prompt = "東京工業大学の主なキャンパスは、"
	input_ids = tokenizer.encode(
	prompt,
	add_special_tokens=False,
	return_tensors="pt"
	)
	tokens = model.generate(
	input_ids.to(device=model.device),
	max_new_tokens=128,
	temperature=0.99,
	top_p=0.95,
	do_sample=True,
	)

	out = tokenizer.decode(tokens[0], skip_special_tokens=True)
	print(out)
	```

	## Training Datasets

	### Continual Pre-Training
	The following datasets were used for continual pre-training.

	- [Algebraic Stack](https://huggingface.co/datasets/EleutherAI/proof-pile-2)
	- [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch)
	- [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
	- [Swallow Corpus](https://chokkan.org/temp/tokyotech-llm/swallow-corpus)
	- [The Pile](https://huggingface.co/datasets/EleutherAI/pile)
	- [The Vault](https://github.com/FSoft-AI4Code/TheVault)

	## Risks and Limitations

	The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.

	## Acknowledgements

	We thank Mistral AI for releasing Mixtral-8x7B-Instruct-v0.1 under an open license for others to build on.

	Our project is supported by the [ABCI Large-scale Language Model Building Support Program](https://abci.ai/en/link/llm_support_program.html) of the National Institute of Advanced Industrial Science and Technology.

	## License

	apache-2.0

	## Authors

	Here are the team members:
	- From [Okazaki Laboratory](https://www.nlp.c.titech.ac.jp/index.en.html), the following members:
	- [Naoaki Okazaki](https://www.chokkan.org/index.ja.html)
	- [Sakae Mizuki](https://s-mizuki-nlp.github.io/)
	- [Hiroki Iida](https://meshidenn.github.io/)
	- [Mengsay Loem](https://loem-ms.github.io/)
	- [Shota Hirai](https://huggingface.co/Kotemo428)
	- [Kakeru Hattori](https://aya-se.vercel.app/)
	- [Masanari Ohi](https://twitter.com/stjohn2007)
	- From [YOKOTA Laboratory](https://www.rio.gsic.titech.ac.jp/en/index.html), the following members:
	- [Rio Yokota](https://twitter.com/rioyokota)
	- [Kazuki Fujii](https://twitter.com/okoge_kaz)
	- [Taishi Nakamura](https://twitter.com/Setuna7777_2)