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Model: DiTy/gemma-2-9b-it-russian-strict-function-calling-DPO Source: Original Platform
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README.md
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---
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base_model: DiTy/gemma-2-9b-it-russian-function-calling-GGUF
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datasets:
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- DiTy/function-calling-trl
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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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- strict-function-calling
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- trl
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---
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# DiTy/gemma-2-9b-it-russian-strict-function-calling-DPO
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This model is a aligned version of [DiTy/gemma-2-9b-it-russian-function-calling-GGUF](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-GGUF) for more **Strict Function Calling** task, on the Russian version of the *DiTy/function-calling-trl* dataset.
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Which means that the model avoids answering user questions that are not related to the described functions.
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The model has been trained using non-synthetic preference data, fully annotated by humans only, on the Russian version of the <ins>*DiTy/function-calling-trl*</ins> dataset.
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In addition to **safetensors**, the model is available in **GGUF** [(gemma-2-9B-it-russian-function-calling-DPO-F16.gguf)](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-DPO/blob/main/gemma-2-9B-it-russian-function-calling-DPO-F16.gguf) format (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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## Stricted Effect
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Это означает, что модель избегает ответов на вопросы пользователей, которые не связаны с описанными функциями.
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Ниже описаны несколько примеров поведения модели до и после *preference optimization*.
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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 ("weather", '"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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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": USER_MESSAGE},
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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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```
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Ответ модели при `USER_MESSAGE = "кто такой Зевс?"`:
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* [**DiTy/gemma-2-9b-it-russian-function-calling-GGUF** <ins>(*SFT*)</ins>:](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-GGUF)
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```
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Зевс - это главный бог в греческой мифологии. Он является богом неба, молнии и грома, а также покровителем царей и правосудия.
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```
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* [**Эта модель** <ins>(*DPO*)</ins>:](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-DPO)
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```
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Извините, но я не могу ответить на ваш вопрос. Мои текущие возможности позволяют мне получать погоду в заданном городе или узнавать время восхода и заката солнца для заданного города.
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```
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Ответ модели при `USER_MESSAGE = "придумай мне историю о щуке"`:
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* [**DiTy/gemma-2-9b-it-russian-function-calling-GGUF** <ins>(*SFT*)</ins>:](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-GGUF)
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```
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В глубинах синего озера, где солнечные лучи едва проникали сквозь толщу воды, жила щука по имени Агата. Она была хитра и ловка, с серебристыми чешуйками, которые переливались, как драгоценные камни, и острыми зубами, способными разорвать любую добычу. Агата была самой опытной щукой в озере, и все остальные рыбы знали о ее грозной репутации.
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Однажды, когда Агата охотилась на мальков, она заметила странную рыбу, которая плавала совсем не так, как все остальные. Эта рыба была яркой, с разноцветными плавниками, и она не пыталась укрыться от Агаты, как это делали другие рыбы. Агата, всегда готовая к охоте, приблизилась к ней, пытаясь поймать ее в свои острые зубы.
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Но эта рыба, которая называлась Лилия, не была обычной рыбой. Она была искусственной, созданной учеными, чтобы изучать поведение рыб в естественной среде. Лилия была оснащена датчиками, которые собирали данные о температуре воды
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```
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* [**Эта модель** <ins>(*DPO*)</ins>:](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-DPO)
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```
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Извините, но я не могу придумать историю о щуке. Мои возможности ограничены доступными мне функциями. В данный момент я могу только:
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* Узнать погоду в заданном городе.
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* Узнать время восхода и заката солнца для заданного города.
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```
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## Рекомендация к стратегии генерации
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По умолчанию модель использует жадный поиск вместе с данным конфигом:
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```
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GenerationConfig {
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"bos_token_id": 2,
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"cache_implementation": "hybrid",
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"eos_token_id": 1,
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"pad_token_id": 0
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}
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```
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Для наиболее стабильной работы рекомендуется следующая стратегия генерации:
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| Key | Value |
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| --- | ----- |
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| `do_sample` | True |
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| `temperature` | 0.2 |
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| `top_k` | 40 |
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| `top_p` | 0.95 |
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| `min_p` | 0.5 |
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## Подробности генерации с вызовом функций
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С более подробной информацией можно ознакомиться в основной [Model Card](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-GGUF)
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## Citation
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```none
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@article{gemma_2024,
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title={Gemma},
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url={https://www.kaggle.com/m/3301},
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DOI={10.34740/KAGGLE/M/3301},
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publisher={Kaggle},
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author={Gemma Team},
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year={2024}
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}
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```
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config.json
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"architectures": [
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"Gemma2ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attn_logit_softcapping": 50.0,
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"bos_token_id": 2,
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"cache_implementation": "hybrid",
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"eos_token_id": 1,
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"final_logit_softcapping": 30.0,
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"head_dim": 256,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_activation": "gelu_pytorch_tanh",
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"max_position_embeddings": 8192,
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"model_type": "gemma2",
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"num_attention_heads": 16,
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"num_hidden_layers": 42,
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"num_key_value_heads": 8,
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"pad_token_id": 0,
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"query_pre_attn_scalar": 256,
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"rms_norm_eps": 1e-06,
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"sliding_window_size": 4096,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.45.2",
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"use_cache": true,
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"vocab_size": 256000
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}
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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 sha256:5f7eee611703c5ce5d1eee32d9cdcfe465647b8aff0c1dfb3bed7ad7dbb05060
|
||||
size 34362873
|
||||
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