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README.md
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# Model Card for Model ID
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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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[More Information Needed]
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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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### 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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### 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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[More Information Needed]
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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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### Framework versions
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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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# 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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RuArenaHard vs gpt-4o:
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