Vikhrmodels
/

Vikhr-Gemma-2B-instruct

@@ -1,199 +1,123 @@
 ---
 library_name: transformers
 tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
 library_name: transformers
+model_name: Vikhr-Gemma-2B-instruct
+base_model:
+  - google/gemma-2-2b-it
 tags: []
+language:
+  - ru
+license: apache-2.0
 ---
+# 🌪️ Vikhr-Gemma-2B-instruct
+#### RU
+Мощная инструктивная модель на основе Gemma 2 2B, обученная на русскоязычном датасете GrandMaster-PRO-MAX.
+#### EN
+A powerful instructive model based on Gemma 2 2B, trained on the Russian-language dataset GrandMaster-PRO-MAX.
+## Особенности:
+- 📚 Основа / Base: [gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it)
+- 🇷🇺 Специализация / Specialization: **RU**
+- 💾 Датасет / Dataset: [GrandMaster-PRO-MAX](https://huggingface.co/datasets/Vikhrmodels/GrandMaster-PRO-MAX)
+## Попробовать / Try now:
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1htw3x1OS73vIJrMYvdQfflGg4ASdGg9P)
+## Описание:
+#### RU
+Vikhr-Gemma-2B-instruct — это мощная и компактная языковая модель, обученная на датасете GrandMaster-PRO-MAX, специально разработанная для обработки русского языка. Эта модель способна решать широкий спектр задач, включая генерацию текста, ответы на вопросы, создание диалогов и выполнение инструкций на русском языке.
+#### EN
+Vikhr-Gemma-2B-instruct is a powerful and compact language model trained on the GrandMaster-PRO-MAX dataset, specifically designed for processing the Russian language. This model is capable of solving a wide range of tasks, including text generation, question answering, dialogue creation, and executing instructions in Russian.
+## Пример кода для запуска / Sample code to run:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+# Загрузка модели и токенизатора
+model_name = "Vikhrmodels/Vikhr-Gemma-2B-instruct"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+# Подготовка входного текста
+input_text = "Напиши стихотворение о весне в России."
+# Токенизация и генерация текста
+input_ids = tokenizer.encode(input_text, return_tensors="pt")
+output = model.generate(input_ids, max_length=200, num_return_sequences=1, no_repeat_ngram_size=2)
+# Декодирование и вывод результата
+generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+print(generated_text)
+```
+#### Ответ модели / Model response:
+> Весна в России – это время обновления природы, когда природа пробуждается >от зимнего сна. Вот стихотворение, отражающее эту красоту:
+>
+> ---
+>
+> **Весна в России**
+>
+> Зимняя тишина утихла,
+> Весна в России пришла.
+> Солнце светит, словно в сказке,
+> В небесах – птицы в полете.
+>
+> Снег пошел, ушел вдаль,
+> И в каждом уголке – весна.
+> Лед промерз, вода в реке –
+> Ветры вьют, и листья поют.
+>
+> Цветы распустились, как будто
+> В честь весны, в честь жизни.
+> Зеленая трава, как полотно,
+> Под ногами – мягкость.
+>
+> Весна в России – это чудо,
+> Счастье, что в сердце живет.
+> И каждый день – праздник,
+> Когда природа в цвету.
+>
+> ---
+>
+> Надеюсь, это стихотворение передало дух и красоту весны в России.
+## Метрики на ru_arena_general / Metrics on ru_arena_general
+| Model                                          | Score     | 95% CI          | Avg Tokens | Std Tokens | LC Score  |
+| ---------------------------------------------- | --------- | --------------- | ---------- | ---------- | --------- |
+| suzume-llama-3-8B-multilingual-orpo-borda-half | 90.89     | +1.1 / -1.1     | 2495.38    | 1211.62    | 55.86     |
+| mistral-nemo-instruct-2407                     | 50.53     | +2.5 / -2.2     | 403.17     | 321.53     | 50.08     |
+| sfr-iterative-dpo-llama-3-8b-r                 | 50.06     | +2.1 / -2.1     | 516.74     | 316.84     | 50.01     |
+| gpt-3.5-turbo-0125                             | 50.00     | +0.0 / -0.0     | 220.83     | 170.30     | 50.00     |
+| glm-4-9b-chat                                  | 49.75     | +1.9 / -2.3     | 568.81     | 448.76     | 49.96     |
+| c4ai-command-r-v01                             | 48.95     | +2.6 / -1.7     | 529.34     | 368.98     | 49.85     |
+| llama-3-instruct-8b-sppo-iter3                 | 47.45     | +2.0 / -2.2     | 502.27     | 304.27     | 49.63     |
+| **Vikhrmodels-vikhr-gemma-2b-it**              | **45.82** | **+2.4 / -2.0** | **722.83** | **710.71** | **49.40** |
+| suzume-llama-3-8b-multilingual                 | 45.71     | +2.4 / -1.7     | 641.18     | 858.96     | 49.38     |
+| yandex_gpt_pro                                 | 45.11     | +2.2 / -2.5     | 345.30     | 277.64     | 49.30     |
+| hermes-2-theta-llama-3-8b                      | 44.07     | +2.0 / -2.2     | 485.99     | 390.85     | 49.15     |
+| gpt-3.5-turbo-1106                             | 41.48     | +1.9 / -2.0     | 191.19     | 177.31     | 48.77     |
+| llama-3-smaug-8b                               | 40.80     | +2.1 / -1.6     | 524.02     | 480.56     | 48.68     |
+| llama-3-8b-saiga-suzume-ties                   | 39.94     | +2.0 / -1.7     | 763.27     | 699.39     | 48.55     |
+```
+@article{nikolich2024vikhr,
+  title={Vikhr: The Family of Open-Source Instruction-Tuned Large Language Models for Russian},
+  author={Aleksandr Nikolich and Konstantin Korolev and Sergey Bratchikov and Nikolay Kompanets and Artem Shelmanov},
+  journal={arXiv preprint arXiv:2405.13929},
+  year={2024},
+  url={https://arxiv.org/pdf/2405.13929}
+}
+```