Instructions to use Xennon-BD/Doctor-Chad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Xennon-BD/Doctor-Chad with Transformers:

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

pipe = pipeline("text-generation", model="Xennon-BD/Doctor-Chad")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Xennon-BD/Doctor-Chad")
model = AutoModelForCausalLM.from_pretrained("Xennon-BD/Doctor-Chad", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Xennon-BD/Doctor-Chad with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Xennon-BD/Doctor-Chad

SGLang

How to use Xennon-BD/Doctor-Chad 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 "Xennon-BD/Doctor-Chad" \
    --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": "Xennon-BD/Doctor-Chad",
		"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 "Xennon-BD/Doctor-Chad" \
        --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": "Xennon-BD/Doctor-Chad",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Xennon-BD/Doctor-Chad with Docker Model Runner:
```
docker model run hf.co/Xennon-BD/Doctor-Chad
```

ar08

SFconvertbot commited on Mar 14, 2025

Commit

f5238dd

verified ·

1 Parent(s): 56d9712

Adding `safetensors` variant of this model (#1)

Browse files

- Adding `safetensors` variant of this model (9bb75a50039fe6555b07ed720ff0f2cb2534a70d)

Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>

Files changed (1) hide show

model.safetensors +3 -0

model.safetensors ADDED Viewed

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