attn-signs
/

GPTR-8b-v1

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- reasoning
+license: apache-2.0
+datasets:
+- d0rj/gsm8k-ru
+language:
+- ru
+base_model:
+- attn-signs/GPTR-8-base
 ---
+# GPT Reasoner (V1)
+- [EN]
+Reasoning model adapted for russian text generation.
+**Based on YandexGPT-pretrain -> GPTR-8-base**
+- [RU]
+Модель рассуждений, адаптированная для генерации русскоязычного текста.
+**Построена на YandexGPT-pretrain -> GPTR-8-base**
+## Model Details / Детализация модели
+- [EN]
+**Reinforced GRPO version** to invoke general reasoning capabilities on a specific system prompt.
+This model can generate conditional and coherent chain-of-thought
+- [RU]
+**Версия RL GRPO** для возможностей размышления и глубокого понимания запроса.
+Модель может генерировать когерентный текст русского языка на этой итерации.
+### Important:
+- [EN]
+This is the first stage of reinforcement learning, don't expect the model to solve every mathematical problem.
+The training is ongoing. Still, this model is stable and can solve something now.
+- [RU]
+Это первая стадия RL обучения, поэтому не стоит возлагать надежды на решения любой математической проблемы данной моделью.
+Обучение продолжается, данная версия модели скорее proof-of-concept, чем готовый математический ассистент.
+Несмотря на это, модель стабильна.
+### 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**: ~24h of NVIDIA A100
+Для обучения использовался HuggingFace Accelerator
+**GPU часы**: ~24h часа NVIDIA A100
+### Training Framework
+**GPTR was trained using MyLLM framework (by Attention Signs):**
+--==[MyLLM](https://github.com/Raumberg/myllm)==--
+### Model configuration (MyLLM Framework)
+TO BE DISCLOSED
+### Using the model / Как запустить?
+```python
+repo = 'attn-signs/GPTR-8-v1'
+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)
+```