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Notice
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Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.
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300
README.md
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README.md
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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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license: llama2
|
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model_type: llama
|
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---
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# Swallow
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||||
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Our Swallow model has undergone continual pre-training from the [Llama 2 family](https://huggingface.co/meta-llama), primarily with the addition of Japanese language data. The tuned versions use supervised fine-tuning (SFT).
|
||||
Links to other models can be found in the index.
|
||||
|
||||
# Model Release Updates
|
||||
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||||
We are excited to share the release schedule for our latest models:
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||||
- **April 26, 2024**: Released version 0.1 of our enhanced instruction-tuned models: [Swallow-7b-instruct-v0.1](https://huggingface.co/tokyotech-llm/Swallow-7b-instruct-v0.1), [Swallow-13b-instruct-v0.1](https://huggingface.co/tokyotech-llm/Swallow-13b-instruct-v0.1), and [Swallow-70b-instruct-v0.1](https://huggingface.co/tokyotech-llm/Swallow-70b-instruct-v0.1) as preview versions.
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- **March 2, 2024**: Released the [Swallow-7b-plus-hf](https://huggingface.co/tokyotech-llm/Swallow-7b-plus-hf), a model trained with approximately twice as many Japanese tokens as [Swallow-7b-hf](https://huggingface.co/tokyotech-llm/Swallow-7b-hf).
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- **February 4, 2024**: Released the [Swallow-13b-NVE-hf](https://huggingface.co/tokyotech-llm/Swallow-13b-NVE-hf).
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- **January 26, 2024**: Released the [Swallow-7b-NVE-hf](https://huggingface.co/tokyotech-llm/Swallow-7b-NVE-hf), [Swallow-7b-NVE-instruct-hf](https://huggingface.co/tokyotech-llm/Swallow-7b-NVE-instruct-hf), [Swallow-70b-NVE-hf](https://huggingface.co/tokyotech-llm/Swallow-70b-NVE-hf), and [Swallow-70b-NVE-instruct-hf](https://huggingface.co/tokyotech-llm/Swallow-70b-NVE-instruct-hf)
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- **December 19, 2023**: Released the [Swallow-7b-hf](https://huggingface.co/tokyotech-llm/Swallow-7b-hf), [Swallow-7b-instruct-hf](https://huggingface.co/tokyotech-llm/Swallow-7b-instruct-hf), [Swallow-13b-hf](https://huggingface.co/tokyotech-llm/Swallow-13b-hf), [Swallow-13b-instruct-hf](https://huggingface.co/tokyotech-llm/Swallow-13b-instruct-hf), [Swallow-70b-hf](https://huggingface.co/tokyotech-llm/Swallow-70b-hf), and [Swallow-70b-instruct-hf](https://huggingface.co/tokyotech-llm/Swallow-70b-instruct-hf).
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## Swallow Model Index
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|Model|Swallow-hf|Swallow-instruct-hf|Swallow-instruct-v0.1|
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|---|---|---|---|
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|7B| [Link](https://huggingface.co/tokyotech-llm/Swallow-7b-hf) | [Link](https://huggingface.co/tokyotech-llm/Swallow-7b-instruct-hf)|[Link](https://huggingface.co/tokyotech-llm/Swallow-7b-instruct-v1.0)|
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|7B-Plus| [Link](https://huggingface.co/tokyotech-llm/Swallow-7b-plus-hf) | N/A | N/A |
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|13B| [Link](https://huggingface.co/tokyotech-llm/Swallow-13b-hf) | [Link](https://huggingface.co/tokyotech-llm/Swallow-13b-instruct-hf)| [Link](https://huggingface.co/tokyotech-llm/Swallow-13b-instruct-v1.0)|
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|70B| [Link](https://huggingface.co/tokyotech-llm/Swallow-70b-hf) | [Link](https://huggingface.co/tokyotech-llm/Swallow-70b-instruct-hf)| [Link](https://huggingface.co/tokyotech-llm/Swallow-70b-instruct-v1.0)|
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## Swallow Model Index NVE (No Vocabulary Expansion)
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|Model|Swallow-NVE-hf|Swallow-NVE-instruct-hf|
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|---|---|---|
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|7B| [Link](https://huggingface.co/tokyotech-llm/Swallow-7b-NVE-hf) | [Link](https://huggingface.co/tokyotech-llm/Swallow-7b-NVE-instruct-hf)|
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|13B| [Link](https://huggingface.co/tokyotech-llm/Swallow-13b-NVE-hf) | N/A |
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|70B| [Link](https://huggingface.co/tokyotech-llm/Swallow-70b-NVE-hf) | [Link](https://huggingface.co/tokyotech-llm/Swallow-70b-NVE-instruct-hf)|
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This repository provides large language models developed by [TokyoTech-LLM](https://tokyotech-llm.github.io/).
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Read our [blog post](https://zenn.dev/tokyotech_lm/articles/d6cb3a8fdfc907) or our [paper](https://arxiv.org/abs/2404.17790)
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## Model Details
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* **Model type**: Please refer to LLaMA-2 technical report for details on the model architecture.
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* **Language(s)**: Japanese English
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* **Library**: [Megatron-LM](https://github.com/rioyokotalab/Megatron-Llama2)
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* **Tokenizer**: This model employs a tokenizer that features a broadened vocabulary based on Japanese data. This allows for a more efficient representation of text using fewer tokens, leading to a notably faster inference process.
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* **Contact**: swallow[at]nlp.c.titech.ac.jp
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## Base Model Performance
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### Japanese tasks
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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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### English tasks
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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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## Evaluation Benchmarks
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### Japanese evaluation benchmarks
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We used llm-jp-eval(v1.0.0) and JP Language Model Evaluation Harness(commit #9b42d41). The details are as follows:
|
||||
|
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- Multiple-choice question answering (JCommonsenseQA [Kurihara+, 2022])
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||||
- Open-ended question answering (JEMHopQA [Ishii+, 2023])
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||||
- Open-ended question answering (NIILC [Sekine, 2003])
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||||
- Machine reading comprehension (JSQuAD [Kurihara+, 2022])
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||||
- Automatic summarization (XL-Sum [Hasan+, 2021])
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||||
- Machine translation (WMT2020 ja-en [Barrault+, 2020])
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- Machine translation (WMT2020 en-ja [Barrault+, 2020])
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- Mathematical reasoning (MGSM [Shi+, 2023])
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### English evaluation benchmarks
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||||
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We used the Language Model Evaluation Harness(v.0.3.0). The details are as follows:
|
||||
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- Multiple-choice question answering (OpenBookQA [Mihaylov+, 2018])
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||||
- Open-ended question answering (TriviaQA [Joshi+, 2017])
|
||||
- Machine reading comprehension (SQuAD 2.0 [Rajpurkar+, 2018])
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||||
- Commonsense reasoning (XWINO [Tikhonov & Ryabinin, 2021])
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- Natural language inference (HellaSwag [Zellers+, 2019])
|
||||
- Mathematical reasoning (GSM8k [Cobbe+, 2021])
|
||||
|
||||
|
||||
## Usage
|
||||
|
||||
First install additional dependencies in [requirements.txt](./requirements.txt):
|
||||
|
||||
```sh
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
### Use the instruct model
|
||||
|
||||
```python
|
||||
import torch
|
||||
from transformers import AutoTokenizer, AutoModelForCausalLM
|
||||
|
||||
model_name = "tokyotech-llm/Swallow-7b-instruct-hf"
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="auto")
|
||||
|
||||
|
||||
PROMPT_DICT = {
|
||||
"prompt_input": (
|
||||
"以下に、あるタスクを説明する指示があり、それに付随する入力が更なる文脈を提供しています。"
|
||||
"リクエストを適切に完了するための回答を記述してください。\n\n"
|
||||
"### 指示:\n{instruction}\n\n### 入力:\n{input}\n\n### 応答:"
|
||||
|
||||
),
|
||||
"prompt_no_input": (
|
||||
"以下に、あるタスクを説明する指示があります。"
|
||||
"リクエストを適切に完了するための回答を記述してください。\n\n"
|
||||
"### 指示:\n{instruction}\n\n### 応答:"
|
||||
),
|
||||
}
|
||||
|
||||
def create_prompt(instruction, input=None):
|
||||
"""
|
||||
Generates a prompt based on the given instruction and an optional input.
|
||||
If input is provided, it uses the 'prompt_input' template from PROMPT_DICT.
|
||||
If no input is provided, it uses the 'prompt_no_input' template.
|
||||
|
||||
Args:
|
||||
instruction (str): The instruction describing the task.
|
||||
input (str, optional): Additional input providing context for the task. Default is None.
|
||||
|
||||
Returns:
|
||||
str: The generated prompt.
|
||||
"""
|
||||
if input:
|
||||
# Use the 'prompt_input' template when additional input is provided
|
||||
return PROMPT_DICT["prompt_input"].format(instruction=instruction, input=input)
|
||||
else:
|
||||
# Use the 'prompt_no_input' template when no additional input is provided
|
||||
return PROMPT_DICT["prompt_no_input"].format(instruction=instruction)
|
||||
|
||||
# Example usage
|
||||
instruction_example = "以下のトピックに関する詳細な情報を提供してください。"
|
||||
input_example = "東京工業大学の主なキャンパスについて教えてください"
|
||||
prompt = create_prompt(instruction_example, input_example)
|
||||
|
||||
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)
|
||||
|
||||
```
|
||||
|
||||
### Use the base model
|
||||
|
||||
```python
|
||||
import torch
|
||||
from transformers import AutoTokenizer, AutoModelForCausalLM
|
||||
|
||||
model_name = "tokyotech-llm/Swallow-7b-hf"
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
|
||||
|
||||
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.
|
||||
|
||||
- [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch)
|
||||
- [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
|
||||
- [Swallow Corpus](https://arxiv.org/abs/2404.17733)
|
||||
- [The Pile](https://huggingface.co/datasets/EleutherAI/pile)
|
||||
|
||||
|
||||
### Instruction Tuning
|
||||
|
||||
The following datasets were used for the instruction tuning.
|
||||
|
||||
- [Anthropic HH-RLHF](https://huggingface.co/datasets/kunishou/hh-rlhf-49k-ja)
|
||||
- [Databricks Dolly 15-k](https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja)
|
||||
- [OpenAssistant Conversations Dataset](https://huggingface.co/datasets/kunishou/oasst1-89k-ja)
|
||||
|
||||
## 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 Meta Research for releasing Llama 2 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
|
||||
|
||||
Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.
|
||||
|
||||
## 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)
|
||||
|
||||
## How to cite
|
||||
|
||||
If you find our work helpful, please feel free to cite us.
|
||||
|
||||
```
|
||||
@inproceedings{Fujii:COLM2024,
|
||||
title={Continual Pre-Training for Cross-Lingual LLM Adaptation:
|
||||
Enhancing Japanese Language Capabilities},
|
||||
author={Kazuki Fujii and Taishi Nakamura and Mengsay Loem and Hiroki
|
||||
Iida and Masanari Ohi and Kakeru Hattori and Hirai Shota and Sakae
|
||||
Mizuki and Rio Yokota and Naoaki Okazaki},
|
||||
booktitle="Proceedings of the First Conference on Language Modeling",
|
||||
series={COLM},
|
||||
pages="(to appear)",
|
||||
year="2024",
|
||||
month=oct,
|
||||
address={University of Pennsylvania, USA},
|
||||
}
|
||||
|
||||
@inproceedings{Okazaki:COLM2024,
|
||||
title={Building a Large Japanese Web Corpus for Large Language Models},
|
||||
author={Naoaki Okazaki and Kakeru Hattori and Hirai Shota and Hiroki
|
||||
Iida and Masanari Ohi and Kazuki Fujii and Taishi Nakamura and Mengsay
|
||||
Loem and Rio Yokota and Sakae Mizuki},
|
||||
booktitle="Proceedings of the First Conference on Language Modeling",
|
||||
series={COLM},
|
||||
pages="(to appear)",
|
||||
year="2024",
|
||||
month=oct,
|
||||
address={University of Pennsylvania, USA},
|
||||
}
|
||||
```
|
||||
28
config.json
Normal file
28
config.json
Normal file
@@ -0,0 +1,28 @@
|
||||
{
|
||||
"_name_or_path": "tokyotech-llm/Swallow-13b-NVE-hf",
|
||||
"architectures": [
|
||||
"LlamaForCausalLM"
|
||||
],
|
||||
"bos_token_id": 1,
|
||||
"eos_token_id": 2,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 5120,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 13824,
|
||||
"max_position_embeddings": 4096,
|
||||
"max_sequence_length": 4096,
|
||||
"model_type": "llama",
|
||||
"num_attention_heads": 40,
|
||||
"num_hidden_layers": 40,
|
||||
"num_key_value_heads": 40,
|
||||
"pad_token_id": 0,
|
||||
"pretraining_tp": 1,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 10000.0,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.33.2",
|
||||
"use_cache": true,
|
||||
"vocab_size": 32000
|
||||
}
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||
11
generation_config.json
Normal file
11
generation_config.json
Normal file
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"bos_token_id": 1,
|
||||
"do_sample": true,
|
||||
"eos_token_id": 2,
|
||||
"pad_token_id": 0,
|
||||
"temperature": 0.6,
|
||||
"max_length": 4096,
|
||||
"top_p": 0.9,
|
||||
"transformers_version": "4.33.2"
|
||||
}
|
||||
|
||||
3
logo.png
Normal file
3
logo.png
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:6a6948289c43c398d1c7a190767e3c68e30215015f44bdaa497836192fc9bbfa
|
||||
size 1913917
|
||||
3
pytorch_model-00001-of-00003.bin
Normal file
3
pytorch_model-00001-of-00003.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:168934ae3922924695816c5ce2fc58e3f7e4d6e5dd287223cf63cdcde09c2741
|
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size 9948737568
|
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3
pytorch_model-00002-of-00003.bin
Normal file
3
pytorch_model-00002-of-00003.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:a534234c12a91e2b7d164659c9765e1cf8972761665321895625f308c3c391f2
|
||||
size 9904173088
|
||||
3
pytorch_model-00003-of-00003.bin
Normal file
3
pytorch_model-00003-of-00003.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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oid sha256:fe869eeaf8212433006d61c8a868728ab09b1cf8a310b5143ed6ab103340bce4
|
||||
size 6178961293
|
||||
410
pytorch_model.bin.index.json
Normal file
410
pytorch_model.bin.index.json
Normal file
@@ -0,0 +1,410 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_size": 26031733760
|
||||
},
|
||||
"weight_map": {
|
||||
"lm_head.weight": "pytorch_model-00003-of-00003.bin",
|
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"model.embed_tokens.weight": "pytorch_model-00001-of-00003.bin",
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||||
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|
||||
5
requirements.txt
Normal file
5
requirements.txt
Normal file
@@ -0,0 +1,5 @@
|
||||
torch
|
||||
transformers
|
||||
sentencepiece
|
||||
accelerate
|
||||
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|
||||
24
special_tokens_map.json
Normal file
24
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Normal file
@@ -0,0 +1,24 @@
|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
|
||||
3
tokenizer.json
Normal file
3
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@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
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size 1842767
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3
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Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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35
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Normal file
35
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Normal file
@@ -0,0 +1,35 @@
|
||||
{
|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
}
|
||||
Reference in New Issue
Block a user