Version iteration
Browse files- README.md +56 -14
- chat_template.jinja +8 -7
- special_tokens_map.json +0 -36
- tokenizer.json +0 -0
- tokenizer_config.json +18 -79
README.md
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@@ -79,6 +79,49 @@ batch_tokens = tokenizer(texts, padding=True, return_tensors="pt")
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print(batch_tokens["input_ids"])
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```
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---
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## 📦 Files Included
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@@ -87,25 +130,24 @@ print(batch_tokens["input_ids"])
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|---------------------------|------------------------------------------------|
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| `tokenizer.json` | Serialized fast tokenizer definition |
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| `tokenizer_config.json` | Configuration (max length, padding side, etc.) |
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| `special_tokens_map.json` | Special token mappings |
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| `tokenizer.py` | Tokenizer implementation |
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---
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## 🔍 Special Tokens
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| Token
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| `<\|bos\|>`
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| `<\|eos\|>`
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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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@@ -124,6 +166,6 @@ If you use **QiTianTokenizer** in your research or project, please cite it as:
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@misc{QiTianTokenizer,
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title = {QiTianTokenizer: A Universal Multilingual Tokenizer with Chinese–English Optimization},
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author = {Morton Li},
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year = {
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}
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```
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print(batch_tokens["input_ids"])
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```
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### 💬 Chat Template (`apply_chat_template`)
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For chat-style data, you can format a list of messages using `apply_chat_template`:
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```python
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("Morton-Li/QiTianTokenizer-Medium", trust_remote_code=True)
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "你好,介绍一下 QiTianTokenizer。"},
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=False,
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)
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print(text)
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# If you need token ids directly:
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inputs = tokenizer.apply_chat_template(
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messages,
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tokenize=True,
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add_generation_prompt=True,
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enable_thinking=False,
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return_tensors="pt",
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)
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print(inputs["input_ids"])
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```
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**Parameters**
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- `add_generation_prompt`
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- `True`: append the assistant role token (e.g. `<|assistant|>`) at the end, so the model can continue generating.
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- `False`: do not append generation prompt (useful for evaluating full dialogues).
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- `enable_thinking`
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- `True`: wrap the assistant part with a thinking span (e.g. `<|begin_of_think|> ... <|end_of_think|>`), if your training/inference uses it.
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- `False`: keep plain assistant content without the thinking wrapper.
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---
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## 📦 Files Included
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|---------------------------|------------------------------------------------|
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| `tokenizer.json` | Serialized fast tokenizer definition |
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| `tokenizer_config.json` | Configuration (max length, padding side, etc.) |
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| `tokenizer.py` | Tokenizer implementation |
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---
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## 🔍 Special Tokens
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| Token | Purpose |
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|------------------------|----------------------------------------------------|
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| `<\|bos\|>` | Beginning of sequence |
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| `<\|eos\|>` | End of sequence |
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| `<\|eot\|>` | End of turn (marks message boundary) |
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| `<\|pad\|>` | Padding token for batch alignment |
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| `<\|mask\|>` | Masked token for MLM-style objectives |
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| `<\|system\|>` | Defines system or meta-instruction context |
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| `<\|user\|>` | Marks user message boundary in conversational data |
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| `<\|assistant\|>` | Marks assistant message boundary |
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| `<\|begin_of_think\|>` | Begin internal reasoning span |
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| `<\|end_of_think\|>` | End internal reasoning span |
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---
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@misc{QiTianTokenizer,
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title = {QiTianTokenizer: A Universal Multilingual Tokenizer with Chinese–English Optimization},
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author = {Morton Li},
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year = {2026},
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}
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```
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chat_template.jinja
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{
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{
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{% endif %}
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{% endfor %}{% if add_generation_prompt %}
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<|assistant|>:
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{{ bos_token }}{% endif %}
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{{ bos_token }}
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{% for message in messages -%}
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<|{{ message.role }}|>{{ message.content }}<|eot|>
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{%- if not loop.last -%}{{ '\n' }}{% endif %}
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{% endfor %}
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{% if add_generation_prompt -%}
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{{ '\n' }}<|assistant|>
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{%- if enable_thinking %}{{ '\n' }}<|begin_of_think|>{% endif %}
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{% endif %}
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<|user|>",
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"<|assistant|>",
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"<|think|>",
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"<|system|>"
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],
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"bos_token": {
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"content": "<|bos|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "<|eos|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"mask_token": {
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"content": "<|mask|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "<|pad|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
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{
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"
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "<|eos|>",
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"lstrip": false,
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"normalized": false,
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"special": true
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"single_word": false,
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"special": true
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"3": {
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"content": "<|mask|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"4": {
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"content": "<|user|>",
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"special": true
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"5": {
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"content": "<|assistant|>",
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"content": "<|think|>",
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"additional_special_tokens": [
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"<|user|>",
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"<|assistant|>",
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],
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"auto_map": {
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"AutoTokenizer": [
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"tokenizer.QiTianTokenizerFast"
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]
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},
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"bos_token": "<|bos|>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|eos|>",
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"extra_special_tokens": {},
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"mask_token": "<|mask|>",
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<|pad|>",
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{
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"backend": "tokenizers",
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"bos_token": "<|bos|>",
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"eos_token": "<|eos|>",
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"extra_special_tokens": [
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"<|eot|>",
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"<|system|>",
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"<|user|>",
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"<|assistant|>",
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"<|begin_of_think|>",
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"<|end_of_think|>",
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"<|placeholder_0|>",
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"<|placeholder_1|>",
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"<|placeholder_2|>",
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"<|placeholder_3|>",
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"<|placeholder_4|>",
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"<|placeholder_5|>",
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"<|placeholder_6|>",
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"<|placeholder_7|>",
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"<|placeholder_8|>",
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"<|placeholder_9|>"
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],
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"mask_token": "<|mask|>",
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<|pad|>",
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