Instructions to use SL-AI/GRaPE-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SL-AI/GRaPE-Mini with Transformers:

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

pipe = pipeline("text-generation", model="SL-AI/GRaPE-Mini")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("SL-AI/GRaPE-Mini")
model = AutoModelForImageTextToText.from_pretrained("SL-AI/GRaPE-Mini")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SL-AI/GRaPE-Mini with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SL-AI/GRaPE-Mini

SGLang

How to use SL-AI/GRaPE-Mini 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 "SL-AI/GRaPE-Mini" \
    --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": "SL-AI/GRaPE-Mini",
		"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 "SL-AI/GRaPE-Mini" \
        --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": "SL-AI/GRaPE-Mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SL-AI/GRaPE-Mini with Docker Model Runner:
```
docker model run hf.co/SL-AI/GRaPE-Mini
```

Sweaterdog commited on Mar 19

Commit

a3378e9

verified ·

1 Parent(s): 9a66d68

Update README.md

Browse files

Files changed (1) hide show

README.md +18 -0

README.md CHANGED Viewed

@@ -26,6 +26,24 @@ The GRaPE Family was trained on about **14 billion** tokens of data after pre-tr
 GRaPE Flash and Nano are monomodal models, only accepting text. GRaPE Mini being trained most recently supports image and video inputs.
 # How to Run
 I recommend using **LM Studio** for running GRaPE Models, and have generally found these sampling parameters to work best:

 GRaPE Flash and Nano are monomodal models, only accepting text. GRaPE Mini being trained most recently supports image and video inputs.
+***
+## Reasoning Modes
+As GRaPE Mini is the only model that thinks, it has *some* support for reasoning modes. In testing, these modes sometimes work. Likely due to an innefficient dataset formatting for it.
+To use thinking modes, you need an XML tag, `<thinking_mode>`, which can equal these values:
+- **Minimal**: Skip thinking *(does not work most of the time, you'll have to be careful with this one)*
+- **Low**: Think Below 1024 tokens
+- **Medium**: Think between 1024 and 8192 tokens
+- **High**: Think for any amount above 8192 tokens
+In your prompt, place the thinking mode at the *end* of your prompt, like this:
+```
+Build me a website called "Aurora Beats." <thinking_mode=medium
+```
 # How to Run
 I recommend using **LM Studio** for running GRaPE Models, and have generally found these sampling parameters to work best: