Text Generation
Transformers
Safetensors
PyTorch
Russian
transformer
fill-mask
chat
russian
easyformer
custom_code
conversational
Eval Results (legacy)
Instructions to use OpenRussianAI/andrey with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenRussianAI/andrey with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OpenRussianAI/andrey", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelWithLMHead model = AutoModelWithLMHead.from_pretrained("OpenRussianAI/andrey", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use OpenRussianAI/andrey with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenRussianAI/andrey" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenRussianAI/andrey", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OpenRussianAI/andrey
- SGLang
How to use OpenRussianAI/andrey with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "OpenRussianAI/andrey" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenRussianAI/andrey", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "OpenRussianAI/andrey" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenRussianAI/andrey", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use OpenRussianAI/andrey with Docker Model Runner:
docker model run hf.co/OpenRussianAI/andrey
Update README.md
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README.md
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license: mit
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---
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---
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language:
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- ru
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license: mit
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- text-generation
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- chat
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- russian
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- transformer
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- pytorch
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- easyformer
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- custom_code
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- conversational
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datasets:
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- custom
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model_type: transformer
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base_model: OpenRussianAI/andrey
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---
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# Andrey — EasyFormer 1M
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Лёгкий русскоязычный чат-бот на архитектуре EasyFormer.
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Упрощённый decoder-only Transformer, ~1.5M параметров.
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## О модели
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Andrey — компактный русскоязычный чат-бот на собственной архитектуре EasyFormer.
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Снаружи — классический decoder-only Transformer, внутри — упрощения для скорости:
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single-head attention, RMSNorm, FFN×2, weight tying.
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Модель обучается за минуты на CPU, помещается в 6 MB, работает на любом устройстве.
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## Архитектура
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| Параметр | Значение |
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|---|---|
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| Тип | decoder-only Transformer |
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| Параметров | ~1.5M |
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| Слоёв | 5 |
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| d_model | 192 |
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| Голов | 1 (single-head) |
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| FFN | ×2, ReLU |
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| Нормализация | RMSNorm |
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| Контекст | 128 токенов |
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| Токенизатор | char-level, 64 символа |
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| Weight tying | lm_head ↔ tok_emb |
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## Быстрый старт
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```bash
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pip install transformers torch huggingface_hub
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```
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```python
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import json, torch
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from transformers import AutoModelForCausalLM
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from huggingface_hub import hf_hub_download
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REPO = "OpenRussianAI/andrey"
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model = AutoModelForCausalLM.from_pretrained(
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REPO,
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trust_remote_code=True,
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dtype=torch.float32,
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).eval()
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d = json.load(open(hf_hub_download(REPO, "tokenizer.json"), encoding="utf-8"))
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stoi, itos = d["stoi"], {int(k): v for k, v in d["itos"].items()}
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enc = lambda s: [stoi[c] for c in s if c in stoi]
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dec = lambda ids: "".join(itos[i] for i in ids)
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def ask(q, max_new=40, temp=0.6, top_k=20):
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ids = torch.tensor([enc(f"User: {q}\nBot:")], dtype=torch.long)
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out = model.generate(ids, max_new_tokens=max_new, do_sample=True,
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temperature=temp, top_k=top_k)
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text = dec(out[0].tolist())
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return text.split("Bot:", 1)[-1].split("User:", 1)[0].strip()
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print(ask("привет"))
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```
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## Примеры диалогов
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```
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User: привет
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Bot: привет, рад тебя видеть
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User: как дела
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Bot: хорошо, готов помочь
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User: кто ты
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Bot: я лёгкий чат-бот на EasyFormer
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User: что умеешь
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Bot: поддерживать простой разговор
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User: ты работаешь на цп
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Bot: да, я работаю на процессоре
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User: сколько у тебя параметров
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Bot: около миллиона
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User: пока
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Bot: до встречи
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```
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## Обучение
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| Параметр | Значение |
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|---|---|
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| Диалогов | 162 |
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| Токенов | ~7 000 |
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| Эпох | 100 |
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| Батч | 32 |
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| Оптимизатор | AdamW, lr=3e-3 |
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| Устройство | Tesla T4 (CUDA) |
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| Время | ~8.5 минут |
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| Финальный loss | 0.056 |
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## Структура репо
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```
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OpenRussianAI/andrey/
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├── README.md
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├── config.json
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├── configuration_easyformer.py
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├── modeling_easyformer.py
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├── pytorch_model.bin
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└── tokenizer.json
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```
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## Ограничения
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- Шаблонные ответы — модель знает только 162 фразы из датасета.
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- Char-level — возможны опечатки на незнакомых словах.
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- Не умеет: считать, переводить, писать код, рассуждать.
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- Контекст 128 токенов — помнит только последнюю реплику.
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- Нет истории — каждый запрос независим.
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## Лицензия
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MIT
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