Instructions to use Multi-Domain-Expert-Learning/draco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Multi-Domain-Expert-Learning/draco with Transformers:

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

pipe = pipeline("text-generation", model="Multi-Domain-Expert-Learning/draco")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Multi-Domain-Expert-Learning/draco")
model = AutoModelForCausalLM.from_pretrained("Multi-Domain-Expert-Learning/draco", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Multi-Domain-Expert-Learning/draco with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Multi-Domain-Expert-Learning/draco

SGLang

How to use Multi-Domain-Expert-Learning/draco 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 "Multi-Domain-Expert-Learning/draco" \
    --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": "Multi-Domain-Expert-Learning/draco",
		"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 "Multi-Domain-Expert-Learning/draco" \
        --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": "Multi-Domain-Expert-Learning/draco",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Multi-Domain-Expert-Learning/draco with Docker Model Runner:
```
docker model run hf.co/Multi-Domain-Expert-Learning/draco
```

draco

Commit History

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Commit History

Adding `safetensors` variant of this model 1cb2567 verified

Upload tokenizer 56fa503

Upload GPTBigCodeForCausalLM 2dc836c

Upload tokenizer 33bce14

Upload GPTBigCodeForCausalLM 760478d

Upload tokenizer 3814c2f

Upload GPTBigCodeForCausalLM e710939

Upload tokenizer fd6cf0b

Upload GPTBigCodeForCausalLM 4ff5d07

initial commit 99e3975

Adding `safetensors` variant of this model

1cb2567
verified

Upload tokenizer

56fa503

Upload GPTBigCodeForCausalLM

2dc836c

Upload tokenizer

33bce14

Upload GPTBigCodeForCausalLM

760478d

Upload tokenizer

3814c2f

Upload GPTBigCodeForCausalLM

e710939

Upload tokenizer

fd6cf0b

Upload GPTBigCodeForCausalLM

4ff5d07

initial commit

99e3975