Text Generation
Transformers
PyTorch
Safetensors
Russian
gpt2
tiny-model
russian
alphagpt
nano-gpt
experimental
text-generation-inference
Instructions to use prostochel097/alphagpt-photon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prostochel097/alphagpt-photon with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="prostochel097/alphagpt-photon")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("prostochel097/alphagpt-photon") model = AutoModelForCausalLM.from_pretrained("prostochel097/alphagpt-photon") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use prostochel097/alphagpt-photon with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prostochel097/alphagpt-photon" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prostochel097/alphagpt-photon", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/prostochel097/alphagpt-photon
- SGLang
How to use prostochel097/alphagpt-photon 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 "prostochel097/alphagpt-photon" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prostochel097/alphagpt-photon", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "prostochel097/alphagpt-photon" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prostochel097/alphagpt-photon", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use prostochel097/alphagpt-photon with Docker Model Runner:
docker model run hf.co/prostochel097/alphagpt-photon
AlphaGPT-Photon
Сверхкомпактная русскоязычная языковая модель на архитектуре GPT2.
Технические характеристики
| Параметр | Значение |
|---|---|
| Архитектура | GPT2-nano |
| Параметры | 4,634 |
| Размер модели | ~18.1 KB |
| Словарь | 500 токенов |
| Контекст | 32 токена |
| Скрытый размер | 8 |
| Слои | 1 |
| Головы внимания | 1 |
| Активация | gelu_new |
| Обучена на | 53 диалогах |
| Эпох обучения | 500 |
Использование
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Загрузка модели
model_name = "prostochel097/alphagpt-ultramini"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Генерация текста
prompt = "Привет"
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=20,
temperature=0.8,
do_sample=True,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(f"Сгенерировано: {generated_text}")
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