Open-Orca/OpenOrca
Viewer • Updated • 2.94M • 18.9k • 1.59k
How to use MuntasirHossain/Meta-Llama-3-8B-OpenOrca with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="MuntasirHossain/Meta-Llama-3-8B-OpenOrca") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("MuntasirHossain/Meta-Llama-3-8B-OpenOrca")
model = AutoModelForCausalLM.from_pretrained("MuntasirHossain/Meta-Llama-3-8B-OpenOrca", device_map="auto")How to use MuntasirHossain/Meta-Llama-3-8B-OpenOrca with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MuntasirHossain/Meta-Llama-3-8B-OpenOrca"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "MuntasirHossain/Meta-Llama-3-8B-OpenOrca",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/MuntasirHossain/Meta-Llama-3-8B-OpenOrca
How to use MuntasirHossain/Meta-Llama-3-8B-OpenOrca with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "MuntasirHossain/Meta-Llama-3-8B-OpenOrca" \
--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": "MuntasirHossain/Meta-Llama-3-8B-OpenOrca",
"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 "MuntasirHossain/Meta-Llama-3-8B-OpenOrca" \
--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": "MuntasirHossain/Meta-Llama-3-8B-OpenOrca",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use MuntasirHossain/Meta-Llama-3-8B-OpenOrca with Docker Model Runner:
docker model run hf.co/MuntasirHossain/Meta-Llama-3-8B-OpenOrca
Meta-Llama-3-8B-OpenOrca is a fine-tuned version of the meta-llama/Meta-Llama-3-8B on 1.5k subsamples of the OpenOrca dataset.
This model follows the ChatML chat template!
import torch
from transformers import AutoTokenizer, pipeline
model = "MuntasirHossain/Meta-Llama-3-8B-OpenOrca"
tokenizer = AutoTokenizer.from_pretrained(model)
llm = pipeline(
task = "text-generation",
model=model,
eos_token_id=tokenizer.eos_token_id,
torch_dtype=torch.float16,
max_new_tokens=256,
do_sample=True,
device_map="auto",
)
def generate(input_text):
system_prompt = "You are a helpful AI assistant."
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": input_text},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = llm(prompt)
return outputs[0]["generated_text"][len(prompt):]
generate("What is a large language model?")