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

Text Generation
Transformers
Safetensors
English
Japanese
mixtral
text-generation-inference
Inference Endpoints
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+ ---
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+ language:
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+ - en
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+ - ja
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+ library_name: transformers
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+ pipeline_tag: text-generation
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+ tag: moe
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+ license: apache-2.0
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+ ---
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+
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+ # Swallow-MX
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+
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+ Our Swallow-MX model has undergone continuous pre-training from the Mixtral-8x7B-Instruct-v0.1, primarily with the addition of Japanese language data.
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+
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+ ![logo](./logo.png)
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+
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+ ## Model Details
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+
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+ * **Model type**: Please refer to Mixtral technical report for details on the model architecture.
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+ * **Language(s)**: Japanese English
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+ * **Tokenizer**: This model utilizes the same tokenizer as employed by Mixtral-8x7B-Instruct-v0.1.
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+ * **Contact**: swallow[at]nlp.c.titech.ac.jp
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+
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+ ## Base Model Performance
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+
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+ ### Japanese version
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+
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+ |Model|Size|JCommonsenseQA|JEMHopQA|NIILC|JSQuAD|XL-Sum|MGSM|WMT20-en-ja|WMT20-ja-en|
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+ |---|---|---|---|---|---|---|---|---|---|
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+ | | |4-shot|4-shot|4-shot|4-shot|1-shot|4-shot|4-shot|4-shot|
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+ | Llama 2 | 7B | 0.3852 | 0.4240 | 0.3410 | 0.7917 | 0.1905 | 0.0760 | 0.1783 | 0.1738 |
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+ | Swallow | 7B | 0.4808 | 0.5078 | 0.5968 | 0.8573 | 0.1830 | 0.1240 | 0.2510 | 0.1511 |
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+ | Swallow-Plus | 7B | 0.5478 | 0.5493 | 0.6030 | 0.8544 | 0.1806 | 0.1360 | 0.2568 | 0.1441 |
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+ | Swallow-NVE | 7B | 0.5433 | 0.5425 | 0.5729 | 0.8684 | 0.2117 | 0.1200 | 0.2405 | 0.1512 |
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+ | Llama 2 | 13B | 0.6997 | 0.4415 | 0.4170 | 0.8533 | 0.2139 | 0.1320 | 0.2146 | 0.1982 |
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+ | Swallow | 13B | 0.7837 | 0.5063 | 0.6398 | 0.9005 | 0.2168 | 0.2040 | 0.2720 | 0.1771 |
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+ | Swallow-NVE | 13B | 0.7712 | 0.5438 | 0.6351 | 0.9030 | 0.2294 | 0.2120 | 0.2735 | 0.1817 |
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+ | Llama 2 | 70B | 0.8686 | 0.4656 | 0.5256 | 0.9080 | 0.2361 | 0.3560 | 0.2643 | **0.2398** |
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+ | Swallow | 70B | 0.9348 | **0.6290** | 0.6960 | 0.9176 | 0.2266 | **0.4840** | **0.3043** | 0.2298 |
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+ | Swallow-NVE | 70B | **0.9410** | 0.5759 | **0.7024** | **0.9254** | **0.2758** | 0.4720 | 0.3042 | 0.2322 |
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+ |Mixtral-8x7B-v0.1|8x7B|0.8347|0.5335|0.3549|0.8847|0.2192|0.3120|0.1970|0.1987|
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+ |Swallow-MX-NVE|8x7B|0.9258|0.5843|0.5687|0.9148|0.2589|0.4360|0.2705|0.2074|
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+
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+ Please note that Swallow-MX-NVE is not derived from Mixtral-8x7B-v0.1, but rather underwent continued pre-training from Mixtral-8x7B-Instruct-v0.1.
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+
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+ ### English version
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+
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+ |Model|Size|OpenBookQA|TriviaQA|HellaSwag|SQuAD2.0|XWINO|GSM8K|
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+ |---|---|---|---|---|---|---|---|
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+ | | |8-shot|8-shot|8-shot|8-shot|8-shot|8-shot|
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+ | Llama 2 | 7B | 0.3580 | 0.6265 | 0.5860 | 0.3207 | 0.9049 | 0.1410 |
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+ | Swallow | 7B | 0.3180 | 0.4836 | 0.5308 | 0.3125 | 0.8817 | 0.1130 |
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+ | Swallow-Plus | 7B | 0.3280 | 0.4558 | 0.5259 | 0.3134 | 0.8929 | 0.1061 |
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+ | Swallow-NVE | 7B | 0.3180 | 0.5079 | 0.5329 | 0.2919 | 0.8817 | 0.0986 |
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+ | Llama 2 | 13B | 0.3760 | 0.7255 | 0.6148 | 0.3681 | 0.9140 | 0.2403 |
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+ | Swallow | 13B | 0.3500 | 0.5852 | 0.5660 | 0.3406 | 0.9075 | 0.2039 |
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+ | Swallow-NVE | 13B | 0.3460 | 0.6025 | 0.5700 | 0.3478 | 0.9006 | 0.1751 |
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+ | Llama 2 | 70B | **0.4280** | **0.8239** | **0.6742** | 0.3770 | **0.9290** | 0.5284 |
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+ | Swallow | 70B | 0.4220 | 0.7756 | 0.6458 | 0.3745 | 0.9204 | 0.4867 |
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+ | Swallow-NVE | 70B | 0.4240 | 0.7817 | 0.6439 | 0.3451 | 0.9256 | 0.4943 |
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+ |Mixtral-8x7B-v0.1|8x7B|0.3960|0.7989|0.6678|**0.3842**|0.9204|**0.5747**|
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+ |Swallow-MX-NVE|8x7B|0.3740|0.7847|0.6520|0.3801|0.9170|0.5694|
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+
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+ Please note that Swallow-MX-NVE is not derived from Mixtral-8x7B-v0.1, but rather underwent continued pre-training from Mixtral-8x7B-Instruct-v0.1.
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+
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+ ## Usage
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+
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+ First install additional dependencies in [requirements.txt](./requirements.txt):
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+
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+ ```sh
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+ pip install -r requirements.txt
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+ ```
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+
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+ ### Use the base model
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ model_name = "tokyotech-llm/Swallow-MX-NVE-hf"
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+ tokenizer = AutoTokenizer.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
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+
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+ model = AutoModelForCausalLM.from_pretrained(model_name)
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+ prompt = "東京工業大学の主なキャンパスは、"
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+ input_ids = tokenizer.encode(
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+ prompt,
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+ add_special_tokens=False,
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+ return_tensors="pt"
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+ )
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+ tokens = model.generate(
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+ input_ids.to(device=model.device),
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+ max_new_tokens=128,
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+ temperature=0.99,
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+ top_p=0.95,
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+ do_sample=True,
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+ )
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+
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+ out = tokenizer.decode(tokens[0], skip_special_tokens=True)
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+ print(out)
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+ ```
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+
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+ ## Training Datasets
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+
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+ ### Continual Pre-Training
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+ The following datasets were used for continual pre-training.
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+
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+ - [Algebraic Stack](https://huggingface.co/datasets/EleutherAI/proof-pile-2)
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+ - [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch)
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+ - [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
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+ - [Swallow Corpus](https://chokkan.org/temp/tokyotech-llm/swallow-corpus)
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+ - [The Pile](https://huggingface.co/datasets/EleutherAI/pile)
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+ - [The Vault](https://github.com/FSoft-AI4Code/TheVault)
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+
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+ ## Risks and Limitations
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+
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+ 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.
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+
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+ ## Acknowledgements
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+
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+ We thank Mistral AI for releasing Mixtral-8x7B-Instruct-v0.1 under an open license for others to build on.
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+
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+ 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.
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+
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+ ## License
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+
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+ apache-2.0
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+
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+ ## Authors
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+
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+ Here are the team members:
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+ - From [Okazaki Laboratory](https://www.nlp.c.titech.ac.jp/index.en.html), the following members:
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+ - [Naoaki Okazaki](https://www.chokkan.org/index.ja.html)
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+ - [Sakae Mizuki](https://s-mizuki-nlp.github.io/)
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+ - [Hiroki Iida](https://meshidenn.github.io/)
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+ - [Mengsay Loem](https://loem-ms.github.io/)
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+ - [Shota Hirai](https://huggingface.co/Kotemo428)
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+ - [Kakeru Hattori](https://aya-se.vercel.app/)
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+ - [Masanari Ohi](https://twitter.com/stjohn2007)
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+ - From [YOKOTA Laboratory](https://www.rio.gsic.titech.ac.jp/en/index.html), the following members:
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+ - [Rio Yokota](https://twitter.com/rioyokota)
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+ - [Kazuki Fujii](https://twitter.com/okoge_kaz)
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+ - [Taishi Nakamura](https://twitter.com/Setuna7777_2)