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Model: DiTy/gemma-2-9b-it-russian-function-calling-GGUF Source: Original Platform
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---
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base_model: google/gemma-2-9b-it
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datasets:
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- DiTy/function-calling
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language:
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- ru
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library_name: transformers
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license: apache-2.0
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pipeline_tag: text-generation
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tags:
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- conversational
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- gemma2
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- function-calling
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- trl
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---
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# DiTy/gemma-2-9b-it-russian-function-calling-GGUF
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This model is a fine-tuned version of [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) for the **Function Calling** task on non-synthetic data,
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fully annotated by humans only, on the Russian version of the <ins>*DiTy/function-calling*</ins> dataset.
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<!-- Provide a quick summary of what the model is/does. -->
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In addition to **safetensors**, the model is available in **GGUF** formats (in this case, you need to download only a single file (*[how to inference GGUF model](https://github.com/abetlen/llama-cpp-python?tab=readme-ov-file#high-level-api)*)):
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| Filename | Quant type | File Size | Description |
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| -------- | ---------- | --------- | ----------- |
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| [gemma-2-9B-it-russian-function-calling-F16.gguf](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-GGUF/blob/main/gemma-2-9B-it-russian-function-calling-F16.gguf) | F16 | 18.5GB | Base model with float16 |
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## Model card разделы
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* [Как подготовить ваши функции (tools) для *Function Calling*](#prepare_func_call)
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* [Просто используйте chat template для генерации](#just_chat_template)
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* [Prompt структура и ожидаемый контент](#roles)
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* [Оценка моделей под вызов функций](#eval)
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## Использование (HuggingFace Transformers)
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Ниже представлены некоторые фрагменты кода о том, как быстро приступить к запуску модели. Сначала установите библиотеку Transformers с помощью:
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```bash
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pip install -U transformers
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```
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### <a name="prepare_func_call"></a>Как подготовить ваши функции (tools) для *Function Calling*
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Вы должны написать функции (инструменты), используемые моделью, в *коде на Python* и обязательно добавить *Python docstrings*, как в примере ниже:
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```python
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def get_weather(city: str):
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"""
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Функция, которая возвращает погоду в заданном городе.
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Args:
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city: Город, для которого надо узнать погоду.
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"""
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import random
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return "sunny" if random.random() > 0.5 else "rainy"
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def get_sunrise_sunset_times(city: str):
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"""
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Функция, которая возвращает время восхода и заката для заданного города для текущей даты (дата от пользователя не требуется), в формате списка: [sunrise_time, sunset_time].
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Args:
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city: Город, в котором можно узнать время восхода и захода солнца.
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"""
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return ["6:00", "18:00"]
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```
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### <a name="just_chat_template"></a>Просто используйте chat template для генерации
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Далее вам нужно загрузить модель и токенизатор:
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained(
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"DiTy/gemma-2-9b-it-russian-function-calling-GGUF",
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device_map="auto",
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torch_dtype=torch.bfloat16, # use float16 or float32 if bfloat16 is not available to you.
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cache_dir=PATH_TO_MODEL_DIR, # optional
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)
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tokenizer = AutoTokenizer.from_pretrained(
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"DiTy/gemma-2-9b-it-russian-function-calling-GGUF",
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cache_dir=PATH_TO_MODEL_DIR, # optional
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)
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```
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Чтобы получить результат генерации, просто используйте `apply_chat_template`. Чтобы учесть наши написанные функции (инструменты),
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нам нужно передать их в виде списка через атрибут `tools`, а также использовать `add_prompt_generation=True`.
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```python
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history_messages = [
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{"role": "system", "content": "Ты - полезный помощник, имеющий доступ к следующим функциям. Используйте их при необходимости - "},
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{"role": "user", "content": "Привет, не мог бы ты сказать мне, во сколько в Краснодаре восходит солнце?"}
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]
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inputs = tokenizer.apply_chat_template(
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history_messages,
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tokenize=False,
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add_generation_prompt=True, # adding prompt for generation
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tools=[get_weather, get_sunrise_sunset_times], # our functions (tools)
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)
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print(inputs)
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```
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Тогда наш `inputs` будет выглядеть следующим образом:
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```
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<bos><start_of_turn>user
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Ты - полезный помощник, имеющий доступ к следующим функциям. Используйте их при необходимости - {
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"name": "get_weather",
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"description": "Функция, которая возвращает погоду в заданном городе.",
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"parameters": {
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"type": "object",
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"properties": {
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"city": {
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"type": "string",
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"description": "Город, для которого надо узнать погоду."
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}
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},
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"required": [
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"city"
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]
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}
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},
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{
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"name": "get_sunrise_sunset_times",
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"description": "Функция, которая возвращает время восхода и заката для заданного города для текущей даты (дата от пользователя не требуется), в формате списка: [sunrise_time, sunset_time].",
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"parameters": {
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"type": "object",
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"properties": {
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"city": {
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"type": "string",
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"description": "Город, в котором можно узнать время восхода и захода солнца."
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}
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},
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"required": [
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"city"
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]
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}
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}
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Привет, не мог бы ты сказать мне, во сколько в Краснодаре восходит солнце?<end_of_turn>
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<start_of_turn>model
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```
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Теперь мы можем сгенерировать ответ модели.
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Будьте осторожны, потому что после `apply_chat_template` нет необходимости *добавлять специальные токены* во время токенизации.
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Поэтому используем `add_special_tokens=False`:
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```python
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terminator_ids = [
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tokenizer.eos_token_id,
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tokenizer.convert_tokens_to_ids("<end_of_turn>"),
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]
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prompt_ids = tokenizer.encode(inputs, add_special_tokens=False, return_tensors='pt').to(model.device)
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generated_ids = model.generate(
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prompt_ids,
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max_new_tokens=512,
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eos_token_id=terminator_ids,
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bos_token_id=tokenizer.bos_token_id,
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)
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generated_response = tokenizer.decode(generated_ids[0][prompt_ids.shape[-1]:], skip_special_tokens=False) # `skip_special_tokens=False` for debug
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print(generated_response)
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```
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Мы получаем генерацию в виде вызова функции:
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```
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Вызов функции: {"name": "get_sunrise_sunset_times", "arguments": {"city": "Краснодар"}}<end_of_turn>
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```
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Отлично, теперь мы можем получать и обрабатывать результаты с помощью нашей *вызываемой функции*, а затем предоставлять модели ответ *функции*:
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```python
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history_messages = [
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{"role": "system", "content": "Ты - полезный помощник, имеющий доступ к следующим функциям. Используйте их при необходимости - "},
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{"role": "user", "content": "Привет, не мог бы ты сказать мне, во сколько в Краснодаре восходит солнце?"},
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{"role": "function-call", "content": '{"name": "get_sunrise_sunset_times", "arguments": {"city": "Los Angeles"}}'},
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{"role": "function-response", "content": '{"times_list": ["6:00", "18:00"]}'}, # гипотетический ответ от нашей функции
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]
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inputs = tokenizer.apply_chat_template(
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history_messages,
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tokenize=False,
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add_generation_prompt=True, # добавление запроса для генерации
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tools=[get_weather, get_sunrise_sunset_times], # наши функции (tools)
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)
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print(inputs)
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```
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Давайте убедимся, что `inputs` верны:
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```
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<bos><start_of_turn>user
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Ты - полезный помощник, имеющий доступ к следующим функциям. Используйте их при необходимости - {
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"name": "get_weather",
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"description": "Функция, которая возвращает погоду в заданном городе.",
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"parameters": {
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"type": "object",
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"properties": {
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"city": {
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"type": "string",
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"description": "Город, для которого надо узнать погоду."
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}
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},
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"required": [
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"city"
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]
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}
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},
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{
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"name": "get_sunrise_sunset_times",
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"description": "Функция, которая возвращает время восхода и заката для заданного города для текущей даты (дата от пользователя не требуется), в формате списка: [sunrise_time, sunset_time].",
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"parameters": {
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"type": "object",
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"properties": {
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"city": {
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"type": "string",
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"description": "Город, в котором можно узнать время восхода и захода солнца."
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}
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},
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"required": [
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"city"
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]
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}
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}
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Привет, не мог бы ты сказать мне, во сколько в Краснодаре восходит солнце?<end_of_turn>
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<start_of_turn>model
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Вызов функции: {"name": "get_sunrise_sunset_times", "arguments": {"city": "Краснодар"}}<end_of_turn>
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<start_of_turn>user
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Ответ от функции: {"times_list": ["6:00", "18:00"]}<end_of_turn>
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<start_of_turn>model
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```
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Аналогично, мы генерируем ответ модели:
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```python
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prompt_ids = tokenizer.encode(inputs, add_special_tokens=False, return_tensors='pt').to(model.device)
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generated_ids = model.generate(
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prompt_ids,
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max_new_tokens=512,
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eos_token_id=terminator_ids,
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bos_token_id=tokenizer.bos_token_id,
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)
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generated_response = tokenizer.decode(generated_ids[0][prompt_ids.shape[-1]:], skip_special_tokens=False) # `skip_special_tokens=False` for debug
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print(generated_response)
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```
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В результате мы получаем ответ модели:
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```
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В Краснодаре солнце восходит в 6:00 утра и заходит в 18:00 вечера.<end_of_turn>
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```
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## Использование через transformers `pipeline`
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<details>
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<summary>
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Generation via pipeline
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</summary>
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```python
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from transformers import pipeline
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generation_pipeline = pipeline(
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"text-generation",
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model="DiTy/gemma-2-9b-it-russian-function-calling-GGUF",
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model_kwargs={
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|
"torch_dtype": torch.bfloat16, # use float16 or float32 if bfloat16 is not supported for you.
|
||||||
|
"cache_dir": PATH_TO_MODEL_DIR, # OPTIONAL
|
||||||
|
},
|
||||||
|
device_map="auto",
|
||||||
|
)
|
||||||
|
history_messages = [
|
||||||
|
{"role": "system", "content": "Ты - полезный помощник, имеющий доступ к следующим функциям. Используйте их при необходимости - "},
|
||||||
|
{"role": "user", "content": "Привет, не мог бы ты сказать мне, во сколько в Краснодаре восходит солнце?"},
|
||||||
|
{"role": "function-call", "content": '{"name": "get_sunrise_sunset_times", "arguments": {"city": "Краснодар"}}'},
|
||||||
|
{"role": "function-response", "content": '{"times_list": ["6:00", "18:00"]}'}
|
||||||
|
]
|
||||||
|
inputs = generation_pipeline.tokenizer.apply_chat_template(
|
||||||
|
history_messages,
|
||||||
|
tokenize=False,
|
||||||
|
add_generation_prompt=True,
|
||||||
|
tools=[get_weather, get_sunrise_sunset_times],
|
||||||
|
)
|
||||||
|
terminator_ids = [
|
||||||
|
generation_pipeline.tokenizer.eos_token_id,
|
||||||
|
generation_pipeline.tokenizer.convert_tokens_to_ids("<end_of_turn>")
|
||||||
|
]
|
||||||
|
outputs = generation_pipeline(
|
||||||
|
inputs,
|
||||||
|
max_new_tokens=512,
|
||||||
|
eos_token_id=terminator_ids,
|
||||||
|
)
|
||||||
|
print(outputs[0]["generated_text"][len(inputs):])
|
||||||
|
```
|
||||||
|
|
||||||
|
</details>
|
||||||
|
|
||||||
|
## <a name="roles"></a>Prompt структура и ожидаемый контент
|
||||||
|
|
||||||
|
Для наиболее корректной работы модели предполагается, что будет использоваться `apply_chat_template`.
|
||||||
|
Необходимо передать историю сообщений в определенном формате.
|
||||||
|
```python
|
||||||
|
history_messages = [
|
||||||
|
{"role": "...", "content": "..."},
|
||||||
|
...
|
||||||
|
]
|
||||||
|
```
|
||||||
|
|
||||||
|
Для использования доступны следующие роли:
|
||||||
|
|
||||||
|
* `system` - это необязательная роль, ее содержимое всегда размещается в самом начале и перед перечислением функций, доступных модели (инструментов).
|
||||||
|
Вы всегда можете воспользоваться стандартным вариантом, который использовался во время обучения: ***"Ты - полезный помощник, имеющий доступ к следующим функциям. Используйте их при необходимости - "***
|
||||||
|
* `user` - запрос пользователя передается через эту роль.
|
||||||
|
* `function-call` - тело вызова функции передается через эту роль.
|
||||||
|
Хотя модель обучена генерировать вызов функции в виде ***"Вызов функции: {...}\<end_of_turn\>"***, вы все равно должны передать только тело ***"{...}"***
|
||||||
|
в поле *"content"*, поскольку используя `apply_chat_template`, постскриптум в инструкциях добавляется автоматически.
|
||||||
|
* `function-response` - в этой роли мы должны передать ответ нашей функции в поле *"content"* в виде словаря ***'{"name_returnable_value": value}'***.
|
||||||
|
* `model` - содержимое, относящееся к этой роли, считается сгенерированным текстом модели.
|
||||||
|
|
||||||
|
|
||||||
|
### Структура истории чата для *Function Calling*
|
||||||
|
|
||||||
|
```
|
||||||
|
[
|
||||||
|
{"role": "system", "content": "Ты - полезный помощник, имеющий доступ к следующим функциям. Используйте их при необходимости - "},
|
||||||
|
{"role": "user", "content": "Привет, не мог бы ты сказать мне, во сколько в Краснодаре восходит солнце?"},
|
||||||
|
{"role": "function-call", "content": '{"name": "get_sunrise_sunset_times", "arguments": {"city": "Краснодар"}}'},
|
||||||
|
{"role": "function-response", "content": '{"times_list": ["6:00", "18:00"]}'}
|
||||||
|
]
|
||||||
|
```
|
||||||
|
|
||||||
|
Это выглядит как:
|
||||||
|
```
|
||||||
|
<bos><start_of_turn>user
|
||||||
|
Ты - полезный помощник, имеющий доступ к следующим функциям. Используйте их при необходимости - {
|
||||||
|
"name": "get_weather",
|
||||||
|
"description": "Функция, которая возвращает погоду в заданном городе.",
|
||||||
|
"parameters": {
|
||||||
|
"type": "object",
|
||||||
|
"properties": {
|
||||||
|
"city": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "Город, для которого надо узнать погоду."
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"required": [
|
||||||
|
"city"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "get_sunrise_sunset_times",
|
||||||
|
"description": "Функция, которая возвращает время восхода и заката для заданного города для текущей даты (дата от пользователя не требуется), в формате списка: [sunrise_time, sunset_time].",
|
||||||
|
"parameters": {
|
||||||
|
"type": "object",
|
||||||
|
"properties": {
|
||||||
|
"city": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "Город, в котором можно узнать время восхода и захода солнца."
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"required": [
|
||||||
|
"city"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
Привет, не мог бы ты сказать мне, во сколько в Краснодаре восходит солнце?<end_of_turn>
|
||||||
|
<start_of_turn>model
|
||||||
|
Вызов функции: {"name": "get_sunrise_sunset_times", "arguments": {"city": "Краснодар"}}<end_of_turn>
|
||||||
|
<start_of_turn>user
|
||||||
|
Ответ от функции: {"times_list": ["6:00", "18:00"]}<end_of_turn>
|
||||||
|
```
|
||||||
|
|
||||||
|
|
||||||
|
### Структура истории чата для обычного user-model шаблона
|
||||||
|
|
||||||
|
```
|
||||||
|
[
|
||||||
|
{"role": "system", "content": "Ты добрый помощник"},
|
||||||
|
{"role": "user", "content": "Расскажи мне о Москве"}
|
||||||
|
]
|
||||||
|
```
|
||||||
|
|
||||||
|
Это выглядит как:
|
||||||
|
```
|
||||||
|
<bos><start_of_turn>user
|
||||||
|
Ты добрый помощник
|
||||||
|
|
||||||
|
Расскажи мне о Москве<end_of_turn>
|
||||||
|
```
|
||||||
|
|
||||||
|
## <a name="eval"></a>Оценка моделей
|
||||||
|
|
||||||
|
В процессе обучения ошибка валидации была приближена к следующим значениям:
|
||||||
|
|
||||||
|
| **Model** | **Generation Language** | **Approximately Validation Loss** |
|
||||||
|
| :-----: | :-----: | :-----: |
|
||||||
|
| [DiTy/gemma-2-27b-it-function-calling-GGUF](https://huggingface.co/DiTy/gemma-2-27b-it-function-calling-GGUF) | EN | 0.47 |
|
||||||
|
| [**DiTy/gemma-2-9b-it-russian-function-calling-GGUF**](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-GGUF) | **RU** | **0.57** |
|
||||||
|
| [DiTy/gemma-2-9b-it-function-calling-GGUF](https://huggingface.co/DiTy/gemma-2-9b-it-function-calling-GGUF) | EN | 0.5 |
|
||||||
|
| [DiTy/gemma-2-2b-it-function-calling](https://huggingface.co/DiTy/gemma-2-2b-it-function-calling) | EN | 0.66 |
|
||||||
|
|
||||||
|
## Citation
|
||||||
|
|
||||||
|
```none
|
||||||
|
@article{gemma_2024,
|
||||||
|
title={Gemma},
|
||||||
|
url={https://www.kaggle.com/m/3301},
|
||||||
|
DOI={10.34740/KAGGLE/M/3301},
|
||||||
|
publisher={Kaggle},
|
||||||
|
author={Gemma Team},
|
||||||
|
year={2024}
|
||||||
|
}
|
||||||
|
```
|
||||||
34
config.json
Normal file
34
config.json
Normal file
@@ -0,0 +1,34 @@
|
|||||||
|
{
|
||||||
|
"_name_or_path": "google/gemma-2-9b-it",
|
||||||
|
"architectures": [
|
||||||
|
"Gemma2ForCausalLM"
|
||||||
|
],
|
||||||
|
"attention_bias": false,
|
||||||
|
"attention_dropout": 0.0,
|
||||||
|
"attn_logit_softcapping": 50.0,
|
||||||
|
"bos_token_id": 2,
|
||||||
|
"cache_implementation": "hybrid",
|
||||||
|
"eos_token_id": 1,
|
||||||
|
"final_logit_softcapping": 30.0,
|
||||||
|
"head_dim": 256,
|
||||||
|
"hidden_act": "gelu_pytorch_tanh",
|
||||||
|
"hidden_activation": "gelu_pytorch_tanh",
|
||||||
|
"hidden_size": 3584,
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 14336,
|
||||||
|
"max_position_embeddings": 8192,
|
||||||
|
"model_type": "gemma2",
|
||||||
|
"num_attention_heads": 16,
|
||||||
|
"num_hidden_layers": 42,
|
||||||
|
"num_key_value_heads": 8,
|
||||||
|
"pad_token_id": 0,
|
||||||
|
"query_pre_attn_scalar": 256,
|
||||||
|
"rms_norm_eps": 1e-06,
|
||||||
|
"rope_theta": 10000.0,
|
||||||
|
"sliding_window": 4096,
|
||||||
|
"sliding_window_size": 4096,
|
||||||
|
"torch_dtype": "bfloat16",
|
||||||
|
"transformers_version": "4.42.2",
|
||||||
|
"use_cache": true,
|
||||||
|
"vocab_size": 256000
|
||||||
|
}
|
||||||
3
gemma-2-9B-it-russian-function-calling-F16.gguf
Normal file
3
gemma-2-9B-it-russian-function-calling-F16.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:423b74a954d90ecab4cd25cd832eb8aea795a852a3ef464967ac8bf5865e13e7
|
||||||
|
size 18490683264
|
||||||
8
generation_config.json
Normal file
8
generation_config.json
Normal file
@@ -0,0 +1,8 @@
|
|||||||
|
{
|
||||||
|
"_from_model_config": true,
|
||||||
|
"bos_token_id": 2,
|
||||||
|
"cache_implementation": "hybrid",
|
||||||
|
"eos_token_id": 1,
|
||||||
|
"pad_token_id": 0,
|
||||||
|
"transformers_version": "4.42.2"
|
||||||
|
}
|
||||||
3
model-00001-of-00004.safetensors
Normal file
3
model-00001-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:318e44237d2d9fa0f55cc1c26c89fa770e2066cc2875db74af20420315c2fbc5
|
||||||
|
size 4903351912
|
||||||
3
model-00002-of-00004.safetensors
Normal file
3
model-00002-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:1f9c305ce9d0588ac22c71f663cfd3c2ed6f4c64012d5244b12bf5bc5d60fdff
|
||||||
|
size 4947570872
|
||||||
3
model-00003-of-00004.safetensors
Normal file
3
model-00003-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:3cad3352d923c487441ae3b7579623cdad22d365576d1d93f1f5c602ef7916fc
|
||||||
|
size 4962221464
|
||||||
3
model-00004-of-00004.safetensors
Normal file
3
model-00004-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:48752a998e4ec33707d3d576de9d19e0f0a73fd54837686245739f455b46ebe1
|
||||||
|
size 3670322200
|
||||||
471
model.safetensors.index.json
Normal file
471
model.safetensors.index.json
Normal file
@@ -0,0 +1,471 @@
|
|||||||
|
{
|
||||||
|
"metadata": {
|
||||||
|
"total_size": 18483411968
|
||||||
|
},
|
||||||
|
"weight_map": {
|
||||||
|
"model.embed_tokens.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.post_feedforward_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.pre_feedforward_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.post_feedforward_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.pre_feedforward_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.10.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.post_feedforward_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.pre_feedforward_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.11.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.11.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.11.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.11.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.11.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.11.post_feedforward_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.11.pre_feedforward_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.11.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.11.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.11.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.11.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
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|
||||||
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|
||||||
|
}
|
||||||
|
}
|
||||||
34
special_tokens_map.json
Normal file
34
special_tokens_map.json
Normal file
@@ -0,0 +1,34 @@
|
|||||||
|
{
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<start_of_turn>",
|
||||||
|
"<end_of_turn>"
|
||||||
|
],
|
||||||
|
"bos_token": {
|
||||||
|
"content": "<bos>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<eos>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"pad_token": {
|
||||||
|
"content": "<pad>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"unk_token": {
|
||||||
|
"content": "<unk>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:3f289bc05132635a8bc7aca7aa21255efd5e18f3710f43e3cdb96bcd41be4922
|
||||||
|
size 17525357
|
||||||
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
|
||||||
|
size 4241003
|
||||||
2013
tokenizer_config.json
Normal file
2013
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user