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Model: llm-jp/llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0 Source: Original Platform
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README.md
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---
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license: apache-2.0
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language:
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- en
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- ja
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programming_language:
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- C
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- C++
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- C#
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- Go
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- Java
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- JavaScript
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- Lua
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- PHP
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- Python
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- Ruby
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- Rust
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- Scala
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- TypeScript
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library_name: transformers
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pipeline_tag: text-generation
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inference: false
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datasets:
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- databricks/databricks-dolly-15k
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- llm-jp/databricks-dolly-15k-ja
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- llm-jp/oasst1-21k-en
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- llm-jp/oasst1-21k-ja
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- llm-jp/oasst2-33k-en
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- llm-jp/oasst2-33k-ja
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---
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# llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0
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This repository provides large language models developed by [LLM-jp](https://llm-jp.nii.ac.jp/), a collaborative project launched in Japan.
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| Model Variant |
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| :--- |
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|**Instruction models**|
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| [llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0) |
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| [llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0) |
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| [llm-jp-13b-instruct-full-ac_001_16x-dolly-ichikara_004_001_single-oasst-oasst2-v2.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-ac_001_16x-dolly-ichikara_004_001_single-oasst-oasst2-v2.0) |
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| |
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| :--- |
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|**Pre-trained models**|
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| [llm-jp-13b-v2.0](https://huggingface.co/llm-jp/llm-jp-13b-v2.0) |
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Checkpoints format: Hugging Face Transformers
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## Required Libraries and Their Versions
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- torch>=2.3.0
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- transformers>=4.40.1
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- tokenizers>=0.19.1
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- accelerate>=0.29.3
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- flash-attn>=2.5.8
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## Usage
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("llm-jp/llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0")
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model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0", device_map="auto", torch_dtype=torch.bfloat16)
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chat = [
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{"role": "system", "content": "以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。"},
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{"role": "user", "content": "自然言語処理とは何か"},
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]
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tokenized_input = tokenizer.apply_chat_template(chat, add_generation_prompt=True, tokenize=True, return_tensors="pt").to(model.device)
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with torch.no_grad():
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output = model.generate(
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tokenized_input,
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max_new_tokens=100,
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do_sample=True,
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top_p=0.95,
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temperature=0.7,
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repetition_penalty=1.05,
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)[0]
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print(tokenizer.decode(output))
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```
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## Model Details
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- **Model type:** Transformer-based Language Model
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- **Total seen tokens:** 256B
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|Model|Params|Layers|Hidden size|Heads|Context length|
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|:---:|:---:|:---:|:---:|:---:|:---:|
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|13b model|13b|40|5120|40|4096|
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## Training
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- **Pre-training:**
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- **Hardware:** 128 A100 40GB GPUs ([mdx cluster](https://mdx.jp/en/))
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- **Software:** Megatron-LM
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- **Instruction tuning:**
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- **Hardware:** 8 A100 40GB GPUs ([mdx cluster](https://mdx.jp/en/))
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- **Software:** [TRL](https://github.com/huggingface/trl) and [DeepSpeed](https://github.com/microsoft/DeepSpeed)
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## Tokenizer
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The tokenizer of this model is based on [huggingface/tokenizers](https://github.com/huggingface/tokenizers) Unigram byte-fallback model.
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The vocabulary entries were converted from [`llm-jp-tokenizer v2.2 (100k: code20K_en40K_ja60K.ver2.2)`](https://github.com/llm-jp/llm-jp-tokenizer/releases/tag/v2.2).
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Please refer to [README.md](https://github.com/llm-jp/llm-jp-tokenizer) of `llm-ja-tokenizer` for details on the vocabulary construction procedure (the pure SentencePiece training does not reproduce our vocabulary).
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- **Model:** Hugging Face Fast Tokenizer using Unigram byte-fallback model
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- **Training algorithm:** Marging Code/English/Japanese vocabularies constructed with SentencePiece Unigram byte-fallback and reestimating scores with the EM-algorithm.
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- **Training data:** A subset of the datasets for model pre-training
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- **Vocabulary size:** 96,867 (mixed vocabulary of Japanese, English, and source code)
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- The acutal size of vocabulary in the pretrained model is 97,024 due to round-up to multiples of 256.
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## Datasets
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### Pre-training
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The models have been pre-trained using a blend of the following datasets.
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| Language | Dataset | Tokens|
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|:---|:---|---:|
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|Japanese|[Wikipedia](https://huggingface.co/datasets/wikipedia)|1.4B
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||[Common Crawl](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v2)|130.7B
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|English|[Wikipedia](https://huggingface.co/datasets/wikipedia)|4.7B
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||[The Pile](https://huggingface.co/datasets/EleutherAI/pile)|110.3B
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|Codes|[The Stack](https://huggingface.co/datasets/bigcode/the-stack)|8.7B
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### Instruction tuning
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The models have been fine-tuned on the following datasets.
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| Language | Dataset | description |
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|:---|:---|:---|
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|Japanese|[ichikara-instruction-004-001](https://liat-aip.sakura.ne.jp/wp/llm%e3%81%ae%e3%81%9f%e3%82%81%e3%81%ae%e6%97%a5%e6%9c%ac%e8%aa%9e%e3%82%a4%e3%83%b3%e3%82%b9%e3%83%88%e3%83%a9%e3%82%af%e3%82%b7%e3%83%a7%e3%83%b3%e3%83%87%e3%83%bc%e3%82%bf%e4%bd%9c%e6%88%90/llm%e3%81%ae%e3%81%9f%e3%82%81%e3%81%ae%e6%97%a5%e6%9c%ac%e8%aa%9e%e3%82%a4%e3%83%b3%e3%82%b9%e3%83%88%e3%83%a9%e3%82%af%e3%82%b7%e3%83%a7%e3%83%b3%e3%83%87%e3%83%bc%e3%82%bf-%e5%85%ac%e9%96%8b/)| A manually constructed Japanese instruction dataset |
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| |[answer-carefully-001](https://liat-aip.sakura.ne.jp/wp/answercarefully-dataset/)| A manually constructed Japanese instruction dataset focusing on LLMs' safety |
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| |[databricks-dolly-15k-ja](https://huggingface.co/datasets/llm-jp/databricks-dolly-15k-ja)| [databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) translated into Japanese using DeepL |
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| |[oasst1-21k-ja](https://huggingface.co/datasets/llm-jp/oasst1-21k-ja)| A subset of [oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) translated into Japanese using DeepL |
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| |[oasst2-33k-ja](https://huggingface.co/datasets/llm-jp/oasst2-33k-ja)| A subset of [oasst2](https://huggingface.co/datasets/OpenAssistant/oasst2) translated into Japanese using DeepL |
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|English |[databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) | - |
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| |[oasst1-21k-en](https://huggingface.co/datasets/llm-jp/oasst1-21k-en)| A subset of [oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) |
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| |[oasst2-33k-en](https://huggingface.co/datasets/llm-jp/oasst2-33k-en)| A subset of [oasst2](https://huggingface.co/datasets/OpenAssistant/oasst2) |
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## Evaluation
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You can view the evaluation results of several LLMs on this [leaderboard](http://wandb.me/llm-jp-leaderboard). We used [llm-jp-eval](https://github.com/llm-jp/llm-jp-eval) (v1.3.0) for the evaluation.
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Besides, we used LLM-as-a-judge frameworks, [Japanese Vicuna QA Benchmark](https://github.com/ku-nlp/ja-vicuna-qa-benchmark/) and [Japanese MT Bench](https://github.com/Stability-AI/FastChat/tree/jp-stable/fastchat/llm_judge), for evaluation.
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For details, please refer to [our technical blog](https://llm-jp.nii.ac.jp/blog/2024/04/30/v2.0-release.html) (in Japanese).
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## Risks and Limitations
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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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## Send Questions to
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llm-jp(at)nii.ac.jp
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## License
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[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
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## Model Card Authors
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*The names are listed in alphabetical order.*
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Namgi Han, Tatsuya Hiraoka, Hirokazu Kiyomaru, Takashi Kodama, and Hiroshi Matsuda.
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config.json
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}
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54
special_tokens_map.json
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54
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Normal file
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||||
},
|
||||
"3": {
|
||||
"content": "<mask|LLM-jp>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"4": {
|
||||
"content": "<pad|LLM-jp>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"5": {
|
||||
"content": "<CLS|LLM-jp>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"6": {
|
||||
"content": "<SEP|LLM-jp>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"7": {
|
||||
"content": "<EOD|LLM-jp>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"</s|LLM-jp>"
|
||||
],
|
||||
"bos_token": "<s|LLM-jp>",
|
||||
"chat_template": "{% for message in messages %}{% if message['role'] == 'user' %}{{ '\\n\\n### 指示:\\n' + message['content'] }}{% elif message['role'] == 'system' %}{{ '以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。' }}{% elif message['role'] == 'assistant' %}{{ '\\n\\n### 応答:\\n' + message['content'] + eos_token }}{% endif %}{% if loop.last and add_generation_prompt %}{{ '\\n\\n### 応答:\\n' }}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"cls_token": "<CLS|LLM-jp>",
|
||||
"eod_token": "<EOD|LLM-jp>",
|
||||
"eos_token": "<EOD|LLM-jp>",
|
||||
"extra_ids": 0,
|
||||
"mask_token": "<mask|LLM-jp>",
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "<pad|LLM-jp>",
|
||||
"sep_token": "<SEP|LLM-jp>",
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "PreTrainedTokenizerFast",
|
||||
"unk_token": "<unk|LLM-jp>"
|
||||
}
|
||||
Reference in New Issue
Block a user