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---
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license: mit
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datasets:
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- mizinovmv/ru_example_DeepSeek-R1-Distill-Qwen-32B
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language:
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- ru
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base_model:
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- yandex/YandexGPT-5-Lite-8B-pretrain
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pipeline_tag: text-generation
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library_name: peft
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tags:
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- r1
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- reasoning
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- think
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- thinking
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---
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#
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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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[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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[More Information Needed]
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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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license: mit
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datasets:
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- mizinovmv/ru_example_DeepSeek-R1-Distill-Qwen-32B
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- lightblue/reasoning-multilingual-R1-Llama-70B-train
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- Pinkstack/thinking-multilingual-30-23-small-690
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- Vikhrmodels/reasoning-0.01-ru
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- Vikhrmodels/russian_math
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- kristaller486/Nebo-T1-Russian
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language:
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- ru
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- en
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base_model:
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- yandex/YandexGPT-5-Lite-8B-pretrain
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pipeline_tag: text-generation
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library_name: peft
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tags:
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- r1
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- reasoning
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- think
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- thinking
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- reflection
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- russian
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- general
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# Russian r1 / YandexGPT-5-Lite-8B-pretrain
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LoRA-адаптер для модели [YandexGPT-5-Lite-8B-pretrain](https://huggingface.co/yandex/YandexGPT-5-Lite-8B-pretrain)
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обученный на миксе из датасетов реализующих r1 (ризонинг) подход.
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Обученная модель способна имитировать логические размышлению на русском языке по аналогии с тем, как
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это делает `r1` от `DeepSeek` или `o1` от `OpenAI` .
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W&B отчёт: https://api.wandb.ai/links/evilfreelancer/zj6s02v4
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Обучение производилось при помощи утилиты [impruver](https://github.com/EvilFreelancer/impruver) используя конфигурацию
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[YandexGPT/8B_lora_r1](https://github.com/EvilFreelancer/impruver/blob/main/recipes/configs/YandexGPT/8B_lora_r1.yaml).
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На всё про всё ушло примерно 18 часов на RTX 4090, при этом понадобилось 23.5Гб видеопамяти.
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```yaml
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output_dir: ./models/YandexGPT-5-Lite_7B_lora_thinking
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train_path: ./train.YandexGPT-5-Lite_7B_lora_thinking.jsonl
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val_path: ./val.YandexGPT-5-Lite_7B_lora_thinking.jsonl
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datasets:
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- name: mizinovmv/ru_example_DeepSeek-R1-Distill-Qwen-32B
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converter: impruver.instruction_to_messages
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add_global_bos: false
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add_global_eos: false
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mapping:
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instruction: ru_query
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output: response
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- name: lightblue/reasoning-multilingual-R1-Llama-70B-train
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converter: impruver.instruction_to_messages
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add_global_bos: false
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add_global_eos: false
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mapping:
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instruction: translated_prompt
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output: response
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- name: Pinkstack/thinking-multilingual-30-23-full-690
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converter: impruver.instruction_to_messages
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add_global_bos: false
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add_global_eos: false
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- name: Vikhrmodels/reasoning-0.01-ru
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converter: impruver.reasoning_to_messages
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add_global_bos: false
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add_global_eos: false
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- name: Vikhrmodels/russian_math
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converter: impruver.reasoning_to_messages
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add_global_bos: false
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add_global_eos: false
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mapping:
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instruction: task
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reasoning: solution
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output: short answer
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- name: kristaller486/Nebo-T1-Russian
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converter: impruver.reasoning_to_messages
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add_global_bos: false
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add_global_eos: false
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mapping:
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instruction: prompt
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reasoning: think
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output: answer
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model:
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class: transformers.AutoModelForCausalLM
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name: yandex/YandexGPT-5-Lite-8B-pretrain
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load_in_4bit: true
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load_in_8bit: false
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dtype: bf16
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lora:
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r: 8 # higher increases accuracy and memory
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lora_alpha: 16 # usually alpha=2*rank
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lora_dropout: 0
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bias: none
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target_modules: [ 'q_proj', 'v_proj', 'output_proj' ]
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task_type: CAUSAL_LM
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tokenizer:
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class: transformers.AutoTokenizer
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name: yandex/YandexGPT-5-Lite-8B-pretrain
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max_tokens_count: 1400
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special_tokens:
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pad_token_id: 1
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pad_token: <s>
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trainer:
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eval_strategy: steps
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save_strategy: steps
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eval_steps: 1000
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save_steps: 1000
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per_device_train_batch_size: 1
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per_device_eval_batch_size: 1
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gradient_accumulation_steps: 8
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logging_steps: 10
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learning_rate: 0.000005
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num_train_epochs: 2
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lr_scheduler_type: cosine
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warmup_steps: 100
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optim: adamw_torch_4bit
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metric_for_best_model: eval_loss
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load_best_model_at_end: true
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save_total_limit: 2
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seed: 42
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remove_unused_columns: false
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max_grad_norm: 1.0
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weight_decay: 0.01
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torch_compile: false
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```
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