tatsu-lab/alpaca
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How to use Jiraya/zoof-250M-base with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Jiraya/zoof-250M-base") # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Jiraya/zoof-250M-base", dtype="auto")How to use Jiraya/zoof-250M-base with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jiraya/zoof-250M-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Jiraya/zoof-250M-base",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Jiraya/zoof-250M-base
How to use Jiraya/zoof-250M-base with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Jiraya/zoof-250M-base" \
--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": "Jiraya/zoof-250M-base",
"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 "Jiraya/zoof-250M-base" \
--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": "Jiraya/zoof-250M-base",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Jiraya/zoof-250M-base with Docker Model Runner:
docker model run hf.co/Jiraya/zoof-250M-base
Zoof is a family of compact text-only Small Language Models (SLMs) with 250 million parameters. Designed for lightweight text generation and instruction following.
This repository contains two versions:
Please refer to the Zoof repository.
The Zoof models were trained on a rigorous curriculum of high-quality data:
The zoof-250M-chat model was further fine-tuned using Supervised Fine-Tuning (SFT) on a diverse mix of instruction datasets: