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- base_model: models/saiga_yandexgpt_8b_sft_m4_d19_hf
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- library_name: peft
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
 
 
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
 
 
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- ## Uses
 
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
 
 
 
 
 
 
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
 
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- ### Downstream Use [optional]
 
 
 
 
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
 
 
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
 
 
 
 
 
 
 
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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-
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- ### Training Data
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- <!-- 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. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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  ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- [More Information Needed]
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- 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).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- [More Information Needed]
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- ### Compute Infrastructure
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- #### Hardware
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- [More Information Needed]
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- #### Software
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- [More Information Needed]
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- [More Information Needed]
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- **APA:**
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- [More Information Needed]
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- [More Information Needed]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
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- ### Framework versions
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- - PEFT 0.14.0
 
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  ---
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+ language:
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+ - ru
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+ datasets:
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+ - IlyaGusev/saiga_scored
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+ - IlyaGusev/saiga_preferences
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+ license: apache-2.0
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  ---
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+ # Saiga/MistralNemo 12B, Russian fine-tune of Mistral Nemo
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+ Based on [yandex/YandexGPT-5-Lite-8B-pretrain](https://huggingface.co/yandex/YandexGPT-5-Lite-8B-pretrain).
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+ Llama.cpp version: TBA
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+ Colab: TBA
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+ ## Prompt format
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+ v1: Llama format:
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+ ```
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+ <s><|start_header_id|>system<|end_header_id|>
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+ Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.<|eot_id|><|start_header_id|>user<|end_header_id|>
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+ Как дела?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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+ Отлично, а у тебя?<|eot_id|><|start_header_id|>user<|end_header_id|>
 
 
 
 
 
 
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+ Шикарно. Как пройти в библиотеку?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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+ ```
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+ ## Code example
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+ ```python
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+ # Исключительно ознакомительный пример.
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+ # НЕ НАДО ТАК ИНФЕРИТЬ МОДЕЛЬ В ПРОДЕ.
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+ # См. https://github.com/vllm-project/vllm или https://github.com/huggingface/text-generation-inference
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+ import torch
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+ from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
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+ MODEL_NAME = "IlyaGusev/saiga_yandexgpt_8b"
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+ model = AutoModelForCausalLM.from_pretrained(
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+ MODEL_NAME,
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+ load_in_8bit=True,
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+ torch_dtype=torch.bfloat16,
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+ device_map="auto"
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+ )
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+ model.eval()
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+ tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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+ generation_config = GenerationConfig.from_pretrained(MODEL_NAME)
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+ print(generation_config)
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+ inputs = ["Почему трава зеленая?", "Сочини длинный рассказ, обязательно упоминая следующие объекты. Дано: Таня, мяч"]
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+ for query in inputs:
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+ prompt = tokenizer.apply_chat_template([{
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+ "role": "user",
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+ "content": query
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+ }], tokenize=False, add_generation_prompt=True)
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+ data = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
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+ data = {k: v.to(model.device) for k, v in data.items()}
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+ data.pop("token_type_ids", None)
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+ output_ids = model.generate(**data, generation_config=generation_config)[0]
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+ output_ids = output_ids[len(data["input_ids"][0]):]
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+ output = tokenizer.decode(output_ids, skip_special_tokens=True).strip()
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+ print(query)
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+ print(output)
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+ print()
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+ print("==============================")
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+ print()
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+ ```
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+ ## Output examples
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+ ```
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+ User: Почему трава зеленая?
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+ Saiga: TBA
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+ ```
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+ ```
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+ User: Сочини длинный рассказ, обязательно упоминая следующие объекты. Дано: Таня, мяч
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+ Saiga: TBA
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+ ```
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+ ## Versions
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+ v1:
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+ - [440bc91e1f765596efaef8099ffc7ec8d2dbb9c6](https://huggingface.co/IlyaGusev/saiga_nemo_12b/commit/440bc91e1f765596efaef8099ffc7ec8d2dbb9c6)
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+ - Other names: saiga_yandexgpt_8b_sft_m4_d19_smpo_m3_d38
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+ - SFT dataset config: [sft_d19.json](https://github.com/IlyaGusev/saiga/blob/main/configs/datasets/sft_d19.json)
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+ - SFT model config: [saiga_yandexgpt_8b_sft_m4.json](https://github.com/IlyaGusev/saiga/blob/main/configs/models/saiga_yandexgpt_8b_sft_m4.json)
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+ - SMPO dataset config: [pref_d38.json](https://github.com/IlyaGusev/saiga/blob/main/configs/datasets/pref_d38.json)
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+ - SMPO model config: [saiga_yandexgpt_8b_smpo_m3.json](https://github.com/IlyaGusev/saiga/blob/main/configs/models/saiga_yandexgpt_8b_smpo_m3.json)
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+ - SFT wandb: [link](https://wandb.ai/ilyagusev/rulm_self_instruct/runs/xq842j0d)
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+ - SimPO wandb: [link](https://wandb.ai/ilyagusev/rulm_self_instruct/runs/y8kujd6y)
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  ## Evaluation
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+ ### v1:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ PingPong:
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5fc2346dea82dd667bb0ffbc/XOXfgQrc_2fmAXS7D8vUq.png)
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+ RuArenaHard vs gpt-4o:
 
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+ ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/5fc2346dea82dd667bb0ffbc/TlJHaJp-j28PZCL2kM3qU.jpeg)