Instructions to use KringleClaus/Dialog-santa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KringleClaus/Dialog-santa with Transformers:

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

pipe = pipeline("text-generation", model="KringleClaus/Dialog-santa")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KringleClaus/Dialog-santa")
model = AutoModelForCausalLM.from_pretrained("KringleClaus/Dialog-santa")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KringleClaus/Dialog-santa with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KringleClaus/Dialog-santa"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KringleClaus/Dialog-santa",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KringleClaus/Dialog-santa

SGLang

How to use KringleClaus/Dialog-santa 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 "KringleClaus/Dialog-santa" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KringleClaus/Dialog-santa",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "KringleClaus/Dialog-santa" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KringleClaus/Dialog-santa",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KringleClaus/Dialog-santa with Docker Model Runner:
```
docker model run hf.co/KringleClaus/Dialog-santa
```

KringleClaus commited on Oct 10, 2021

Commit

cc08cd6

1 Parent(s): 437ef8f

Upload training_args.bin with git-lfs

Browse files

Files changed (1) hide show

training_args.bin +3 -0

training_args.bin ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:1b30ae7335278e93e57405bb84d308eb05b1aa3d0b8e32575228150d9eb4d28d
+size 1327