Instructions to use RootYuan/opt-1.3b-alpaca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RootYuan/opt-1.3b-alpaca with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="RootYuan/opt-1.3b-alpaca")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("RootYuan/opt-1.3b-alpaca")
model = AutoModelForMultimodalLM.from_pretrained("RootYuan/opt-1.3b-alpaca")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RootYuan/opt-1.3b-alpaca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RootYuan/opt-1.3b-alpaca"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RootYuan/opt-1.3b-alpaca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RootYuan/opt-1.3b-alpaca

SGLang

How to use RootYuan/opt-1.3b-alpaca with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "RootYuan/opt-1.3b-alpaca" \
    --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": "RootYuan/opt-1.3b-alpaca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

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 "RootYuan/opt-1.3b-alpaca" \
        --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": "RootYuan/opt-1.3b-alpaca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RootYuan/opt-1.3b-alpaca with Docker Model Runner:
```
docker model run hf.co/RootYuan/opt-1.3b-alpaca
```

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RootYuan/opt-1.3b-alpaca")
model = AutoModelForCausalLM.from_pretrained("RootYuan/opt-1.3b-alpaca")

usage:

instruction = "Classify the following into animals, plants, and minerals"
input = "Oak tree, copper ore, elephant"

prompts_no_input = f"### Instruction:\n{instruction}\n\n### Response:"
prompts_with_input = f"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"
prompts = prompts_no_input if input is None else prompts_with_input

inputs = tokenizer.encode(prompts, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=64)
ans = tokenizer.decode(outputs[0]).strip('</s>')[len(prompts):]
if input is None:
    print(f"Human: {instruction}")
else:
    print(f"Human: {instruction}\nInput: {input}")
print(f"Assistant: {ans}")

outputs:

Human: Classify the following into animals, plants, and minerals
Input: Oak tree, copper ore, elephant
Assistant: Oak tree: Plant
           Copper ore: Mineral
           Elephant: Animal

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