heegyu/wizard_vicuna_70k_v2
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How to use heegyu/WizardVicuna-3B-0719 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="heegyu/WizardVicuna-3B-0719") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("heegyu/WizardVicuna-3B-0719")
model = AutoModelForCausalLM.from_pretrained("heegyu/WizardVicuna-3B-0719")How to use heegyu/WizardVicuna-3B-0719 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "heegyu/WizardVicuna-3B-0719"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "heegyu/WizardVicuna-3B-0719",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/heegyu/WizardVicuna-3B-0719
How to use heegyu/WizardVicuna-3B-0719 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "heegyu/WizardVicuna-3B-0719" \
--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": "heegyu/WizardVicuna-3B-0719",
"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 "heegyu/WizardVicuna-3B-0719" \
--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": "heegyu/WizardVicuna-3B-0719",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use heegyu/WizardVicuna-3B-0719 with Docker Model Runner:
docker model run hf.co/heegyu/WizardVicuna-3B-0719
Base Model: openlm-research/open_llama_3b
Usage
### Human:
your instruction
### ASSISANT:
output will be generated and ended with <|endoftext|>