prostochel097/ru_qa_dialog
Updated • 2
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")How to use prostochel097/alphagpt-photon with vLLM:
# 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
}'docker model run hf.co/prostochel097/alphagpt-photon
How to use prostochel097/alphagpt-photon with SGLang:
# 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
}'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
}'How to use prostochel097/alphagpt-photon with Docker Model Runner:
docker model run hf.co/prostochel097/alphagpt-photon
Сверхкомпактная русскоязычная языковая модель на архитектуре GPT2.
| 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 |
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}")