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Model: attn-signs/GPTR-8b-v2 Source: Original Platform
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README.md
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---
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library_name: transformers
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tags:
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- reasoning
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license: apache-2.0
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datasets:
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- attn-signs/gromov-1
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language:
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- ru
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base_model:
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- attn-signs/GPTR-8b-v1
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---
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# GPT Reasoner (V2)
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- [EN]
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Reasoning model adapted for russian text generation.
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**Based on YandexGPT-pretrain -> GPTR-8b-base -> GPTR-8b-v1 -> GPTR-8b-v2**
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- [RU]
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Модель рассуждений, адаптированная для генерации русскоязычного текста.
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**Построена на YandexGPT-pretrain -> GPTR-8b-base -> GPTR-8b-v1 -> GPTR-8b-v2**
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## Model Details / Детализация модели
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- [EN]
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**Reinforced GRPO version** to invoke general reasoning capabilities.
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This model can generate conditional and coherent chain-of-thought
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- [RU]
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**Версия RL GRPO** для возможностей размышления и глубокого понимания запроса.
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Модель может генерировать когерентный текст русского языка на этой итерации.
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### Important:
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- [EN]
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Second stage of reinforcement learning to invoke reasoning capabilities.
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The model became better in mathematical tasks.
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- [RU]
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Вторая стадия RL обучения. Модель стала гораздо лучше в решении математических задач
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### Further development
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- GRPO Gromov-2
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### Model Description / Описание модели
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- **Developed by:** [Reisen Raumberg (Attention Signs team)]
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- **Language(s) (NLP):** [RU/EN]
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- **SFT from model:** [YandexGPT-5-lite-8B-pretrain]
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Utilized HF.Accelerator
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**GPU hours**: ~24h of NVIDIA A100
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Для обучения использовался HuggingFace Accelerator
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**GPU часы**: ~24h часа NVIDIA A100
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### Training Framework
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**GPTR was trained using MyLLM framework (by Attention Signs):**
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--==[MyLLM](https://github.com/Raumberg/myllm)==--
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### Model configuration (MyLLM Framework)
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TO BE DISCLOSED
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### Rewards:
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TO BE DISCLOSED
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### Using the model / Как запустить?
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```python
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repo = 'attn-signs/GPTR-8-v2'
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model = AutoModelForCausalLM.from_pretrained(repo)
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tokenizer = AutoTokenizer.from_pretrained(repo)
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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model.to(device)
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user_prompt = '''
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Пусть функция $f:[a,b]\to \mathbb{R}$ непрерывна на $[a,b]$, дифференцируема во всех точках $t\in(a,b)\setminus\{x\}$,
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и существует предел $\lim_{t \to x} f'(t)$. Докажите, что $f$ дифференцируема в точке $x$ и $f'(x)= \lim_{t \to x} f'(t)$.
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Используйте определение $f'(x)$ и теорему о среднем значении для обоснования вашего аргумента.
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'''
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system_prompt = "[MODE: Reflection]"
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=4096
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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```
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Для доказательства того, что $f$ дифференцируема в точке $x$ и $f'(x) = \lim_{t \to x} f'(t)$, мы следуем этим шагам:
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1. **Определение производной в точке $x$:**
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Производная $f$ в точке $x$ определяется как:
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\[
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f'(x) = \lim_{h \to 0} \frac{f(x+h) - f(x)}{h}
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]
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2. **Использование теоремы о среднем значении**:
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Для любого $h \neq 0$ существует $c \in (x, x+h)$ такое, что:
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\[
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\frac{f(x+h) - f(x)}{h} = f'(c)
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\]
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Поскольку $f$ дифференцируема во всех точках $(a, b) \setminus \{x\}$, $f'(c)$ существует.
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3. **Анализ предела при $h \to 0$**:
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Когда $h \to 0$, $c \to x$. Следовательно, $\lim_{h \to 0} f'(c) = \lim_{t \to x} f'(t)$.
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4. **Вывод**:
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Поскольку $\frac{f(x+h) - f(x)}{h} = f'(c)$ и $\lim_{h \to 0} f'(c) = \lim_{t \to x} f'(t)$, имеем:
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\[
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f'(x) = \lim_{h \to 0} \frac{f(x+h) - f(x)}{h} = \lim_{t \to x} f'(t)
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\]
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Таким образом, $f$ дифференцируема в точке $x$ и $f'(x) = \lim_{t \to x} f'(t)$.
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\[
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\boxed{f'(x) = \lim_{t \to x} f'(t)}
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\]
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Я поразмышмышлял над задачей и вот, что я выяснил: используя теорему о среднем значении и предел при $h \to 0$, мы показали, что $f$ дифференцируема в точке $x$ и $f'(x) = \lim_{t \to x} f'(t)$. Таким образом, окончательный ответ:
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\[
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\boxed{f'(x) = \lim_{t \to x} f'(t)}
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\]
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```
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### Model's ability to prove mathematical statements also became better:
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**Question:**
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Докажите, что для независимой семьи подмножеств $\mathcal{A}$ множества $\kappa \geq \omega$ семейство $G_f$,
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определенное как $G_f = \{X : |\kappa - X| < \kappa\} \cup \{X : f(X) = 1\} \cup \{\kappa - X : f(X) = 0\}$ для любой функции $f : \mathcal{A} \to \{0,1\}$,
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обладает свойством конечного пересечения (f.i.p.), и объясните, почему включение $\{X : |\kappa - X| < \kappa\}$ в $G_f$ необходимо для того, чтобы это свойство выполнялось.
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**Answer:**
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```
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Чтобы доказать, что семейство $G_f$ обладает свойством конечного пересечения (f.i.p.), необходимо показать, что любое конечное подсемейство $\{G_{f_1}, G_{f_2}, \ldots, G_{f_n}\}$ из $G_f$ имеет непустое пересечение.
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1. **Понимание $G_f$:**
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- $G_f$ состоит из трёх типов подмножеств:
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- $X$ таких, что $|\kappa - X| < \kappa$ (то есть $X$ является конечным).
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- $X$ таких, что $f(X) = 1$.
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- $\kappa - X$ таких, что $f(X) = 0$.
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2. **Анализ конечного подсемейства:**
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- Рассмотрим конечное подсемейство $\{G_{f_1}, G_{f_2}, \ldots, G_{f_n}\}$.
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- Каждое $G_{f_i}$ содержит подмножества, удовлетворяющие одному из трёх условий:
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- $|\kappa - X| < \kappa$
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- $f_i(X) = 1$
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- $f_i(\kappa - X) = 0$
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3. **Поиск общего подмножества:**
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- Рассмотрим подмножество $X$, которое удовлетворяет $|\kappa - X| < \kappa$.
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- Для каждого $f_i$, $X$ либо удовлетворяет $f_i(X) = 1$, либо $f_i(\kappa - X) = 0$.
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- Следовательно, $X$ принадлежит хотя бы одному из $G_{f_i}$.
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4. **Проверка:**
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- Если $X$ удовлетворяет $|\kappa - X| < \kappa$, то $X$ принадлежит $G_{f_i}$ для всех $i$.
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- Если $X$ не удовлетворяет $|\kappa - X| < \kappa$, то $\kappa - X$ принадлежит $G_{f_i}$ для всех $i$.
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5. **Вывод:**
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- В любом случае, $X$ или $\kappa - X$ принадлежит всем $G_{f_i}$, что делает пересечение непустым.
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Таким образом, $G_f$ обладает свойством конечного пересечения.
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**Необходимость включения $\{X : |\kappa - X| < \kappa\}$:
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- Если исключить $\{X : |\kappa - X| < \kappa\}$ из $G_f$, то $G_f$ может не иметь непустого пересечения для некоторых функций $f$, например, когда $f(X) = 0$ для всех $X$.
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Следовательно, включение $\{X : |\kappa - X| < \kappa\}$ в $G_f$ необходимо для того, чтобы $G_f$ обладало свойством конечного пересечения.
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\[
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\boxed{1} # Model thinks that 1 is True
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\]
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Я поразмышмышлял над задачей и вот, что я выяснил: семейство $G_f$ обладает свойством конечного пересечения благодаря включению подмножеств $\{X : |\kappa - X| < \kappa\}$. Это включение необходимо для того, чтобы гарантировать, что любое конечное подсемейство $\{G_{f_1}, G_{f_2}, \ldots, G_{f_n}\}$ имеет непустое пересечение.
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\[
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\boxed{1}
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\]
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```
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added_tokens.json
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"</think>": 129025,
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"<think>": 129024
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"model_type": "llama",
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.51.1",
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"use_cache": true,
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"vocab_size": 129026
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}
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34
special_tokens_map.json
Normal file
34
special_tokens_map.json
Normal file
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||||
{
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||||
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
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version https://git-lfs.github.com/spec/v1
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size 18023641
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3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
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||||
version https://git-lfs.github.com/spec/v1
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oid sha256:26ced0bb32d59b6f70f555e9f75cdb2c00e36c7dd5b5d5c0944c465dca2db829
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size 2573165
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8292
tokenizer_config.json
Normal file
8292
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user