Instructions to use NaughtyDog97/DFE-GPS-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NaughtyDog97/DFE-GPS-9B with Transformers:

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

pipe = pipeline("text-generation", model="NaughtyDog97/DFE-GPS-9B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("NaughtyDog97/DFE-GPS-9B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NaughtyDog97/DFE-GPS-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NaughtyDog97/DFE-GPS-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NaughtyDog97/DFE-GPS-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NaughtyDog97/DFE-GPS-9B

SGLang

How to use NaughtyDog97/DFE-GPS-9B 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 "NaughtyDog97/DFE-GPS-9B" \
    --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": "NaughtyDog97/DFE-GPS-9B",
		"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 "NaughtyDog97/DFE-GPS-9B" \
        --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": "NaughtyDog97/DFE-GPS-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NaughtyDog97/DFE-GPS-9B with Docker Model Runner:
```
docker model run hf.co/NaughtyDog97/DFE-GPS-9B
```

zezeze97 commited on Aug 28, 2024

Commit

02cb616

1 Parent(s): 97ecdbf

123

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -109,7 +109,7 @@ image = Image.open(img_path).convert('RGB')
 # formalization
 prompt = 'Based on the image, first describe what you see in the figure, then predict the construction_cdl and image_cdl and calibrate it.'
-text = f"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<image>\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
 input_ids = tokenizer_image_token(text, formalization_tokenizer, -200, return_tensors='pt').unsqueeze(0).cuda()
 # generate

 # formalization
 prompt = 'Based on the image, first describe what you see in the figure, then predict the construction_cdl and image_cdl and calibrate it.'
+text = f'<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<image>\n{prompt}<|im_end|>\n<|im_start|>assistant\n'
 input_ids = tokenizer_image_token(text, formalization_tokenizer, -200, return_tensors='pt').unsqueeze(0).cuda()
 # generate