Taishi-N324
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README.md
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license: apache-2.0
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# Swallow-MX
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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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![logo](./logo.png)
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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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| 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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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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### English version
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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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| 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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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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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "tokyotech-llm/Swallow-MX-NVE-
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tokenizer = AutoTokenizer.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
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model = AutoModelForCausalLM.from_pretrained(model_name)
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license: apache-2.0
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---
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# Swallow-MX-8x7b-NVE-v0.1
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Our Swallow-MX-8x7b-NVE-v0.1 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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![logo](./logo.png)
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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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| Mistral-7B-v0.1 | 7B | 0.7301 | 0.4245 | 0.2722 | 0.8563 | 0.2006 | 0.1760 | 0.1405 | 0.1733 |
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|Swallow-MS-7b-v0.1| 7B | 0.8570 | 0.4915 | 0.5519 | 0.8802 | 0.1988 | 0.2240 | 0.2494 | 0.1667 |
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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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| 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-8x7b-NVE-v0.1|8x7B|0.9258|0.5843|0.5687|0.9148|0.2589|0.4360|0.2705|0.2074|
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### English version
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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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| 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 |
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|Swallow-MS-7b-v0.1| 7B | 0.3440 | 0.5976 | 0.5810 | 0.3364 | 0.9037 | 0.2623 |
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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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| 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-8x7b-NVE-v0.1|8x7B|0.3740|0.7847|0.6520|0.3801|0.9170|0.5694|
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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.
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1"
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tokenizer = AutoTokenizer.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
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model = AutoModelForCausalLM.from_pretrained(model_name)
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