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README.md

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+---
+library_name: transformers
+tags:
+- reasoning
+license: apache-2.0
+datasets:
+- attn-signs/gromov-0
+language:
+- ru
+base_model:
+- yandex/YandexGPT-5-Lite-8B-pretrain
+---
+# GPT Reasoner (Base model)
+
+- [EN]  
+Reasoning model adapted for russian text generation.  
+**Based on YandexGPT-pretrain** 
+- [RU]  
+Модель рассуждений, адаптированная для генерации русскоязычного текста.  
+**Построена на YandexGPT-pretrain**
+
+## Model Details / Детализация модели
+- [EN]  
+**Cold-start SFT version** to invoke general reasoning capabilities on a specific system prompt.  
+This model **IS ONLY USED** for further GRPO optimizations, it cannot generate coherent russian text in this iteration.
+- [RU]  
+**Версия cold-start SFT обучения** для возможностей размышления и глубокого понимания запроса.
+Эта модель **ИСПОЛЬЗУЕТСЯ ТОЛЬКО** для дальнейших стадий обучения с GRPO.
+Модель не может генерировать когерентный текст русского языка на этой итерации.
+
+
+### Model Description / Описание модели
+
+- **Developed by:** [Reisen Raumberg (Attention Signs team)]
+- **Language(s) (NLP):** [RU/EN]
+- **SFT from model:** [YandexGPT-5-lite-8B-pretrain]
+
+Utilized HF.Accelerator  
+**GPU hours**: ~3h of NVIDIA A100
+
+Для обучения использовался HuggingFace Accelerator  
+**GPU часы**: ~3 часа NVIDIA A100
+
+### Training Framework
+**GPTR was trained using MyLLM framework (by Attention Signs):**  
+--==[MyLLM](https://github.com/Raumberg/myllm)==--  
+
+### Model configuration (MyLLM Framework)
+Full SFT finetuning
+```toml
+[model]
+model_name_or_path = "yandex/YandexGPT-5-Lite-8B-pretrain"
+
+[datasets]
+dataset = "attn-signs/gromov-0"
+conversation_field = "conversation"
+generate_eval_examples = false
+evaluation_strategy = "steps"
+eval_steps = 100
+dataloader_num_workers = 2
+remove_unused_columns = true
+test_size = 0.05
+
+[run]
+save_strategy = "steps"
+save_steps = 300
+save_total_limit = 3
+run_name = "sft-gptr-8-run2"
+report_to = "wandb"
+logging_first_step = true
+logging_steps = 1
+output_dir = "models/attn-signs-gptr-8-run2"
+project_name = "sft-gptr"
+
+[training]
+train_only_on_completions = true
+per_device_train_batch_size = 1
+per_device_eval_batch_size = 1
+num_train_epochs = 3
+learning_rate = 0.000009
+max_seq_length = 8192
+gradient_accumulation_steps = 8
+gradient_checkpointing = true
+warmup_steps = 10
+bf16 = true
+seed = 42
+use_peft = false
+
+[fusion]
+attn_implementation = "flash_attention_2"
+
+[tokenizer]
+assistant_message_template =  "<s>assistant\n"
+eos_token =  "</s>"
+pad_token = "<unk>"
+chat_template = "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<s>' + message['role'] + '\n' + message['content'] + '</s>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<s>assistant\n' }}{% endif %}"
+force_chat_template = true
+added_special_tokens = [
+    "<think>",
+    "</think>"
+]
+system_prompt = """
+[MODE: Reflection]
+"""
+```
+
+### Using the model / Как запустить?
+
+```python
+repo = 'attn-signs/GPTR-8-base'
+
+model = AutoModelForCausalLM.from_pretrained(repo)
+tokenizer = AutoTokenizer.from_pretrained(repo)
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+model.to(device)
+
+user_prompt = '''
+У уравнений x**2 + 2019ax + b = 0 и x**2 + 2019bx + a = 0 есть один общий корень. Чему может быть равен этот корень, если известно, что a != b?
+'''
+system_prompt = "[MODE: Reflection]"
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": user_prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=4096
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+print(response)
+```