oscar-corpus/oscar
Updated • 717 • 207
How to use Rakshinrules/Raksh with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Rakshinrules/Raksh") # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Rakshinrules/Raksh", dtype="auto")How to use Rakshinrules/Raksh with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rakshinrules/Raksh"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Rakshinrules/Raksh",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Rakshinrules/Raksh
How to use Rakshinrules/Raksh with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Rakshinrules/Raksh" \
--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": "Rakshinrules/Raksh",
"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 "Rakshinrules/Raksh" \
--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": "Rakshinrules/Raksh",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Rakshinrules/Raksh with Docker Model Runner:
docker model run hf.co/Rakshinrules/Raksh
Эта модель предназначена для выполнения задач генерации текста на русском языке. Она разработана с использованием предобученной модели Hugging Face и оптимизирована для работы с текстами средней длины.
from transformers import pipeline
generator = pipeline("text-generation", model="Rakshinrules/Raksh")
response = generator("Пример запроса", max_length=100, num_return_sequences=1)
print(response)
Base model
google-t5/t5-base
Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "Rakshinrules/Raksh"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Rakshinrules/Raksh", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'