Instructions to use fudan-generative-ai/D3D-8B-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fudan-generative-ai/D3D-8B-8B with Transformers:

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

pipe = pipeline("text-generation", model="fudan-generative-ai/D3D-8B-8B", trust_remote_code=True)
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("fudan-generative-ai/D3D-8B-8B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("fudan-generative-ai/D3D-8B-8B", trust_remote_code=True, 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 fudan-generative-ai/D3D-8B-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fudan-generative-ai/D3D-8B-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fudan-generative-ai/D3D-8B-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fudan-generative-ai/D3D-8B-8B

SGLang

How to use fudan-generative-ai/D3D-8B-8B 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 "fudan-generative-ai/D3D-8B-8B" \
    --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": "fudan-generative-ai/D3D-8B-8B",
		"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 "fudan-generative-ai/D3D-8B-8B" \
        --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": "fudan-generative-ai/D3D-8B-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fudan-generative-ai/D3D-8B-8B with Docker Model Runner:
```
docker model run hf.co/fudan-generative-ai/D3D-8B-8B
```

Add pipeline tag and paper link

by nielsr HF Staff - opened 8 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+10

-9

nielsr

8 days ago

This PR adds pipeline_tag: text-generation to the metadata so the model is correctly categorized and discoverable under text generation. It also updates the model description to link to the paper on the Hugging Face paper page. The existing quick start, model details, and links are preserved.

Add pipeline tag and paper linkc41c8a36

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