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

Libraries

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

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

pipe = pipeline("text-generation", model="SL-AI/CRePE-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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("SL-AI/CRePE-Mini")
model = AutoModelForMultimodalLM.from_pretrained("SL-AI/CRePE-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 Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SL-AI/CRePE-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/CRePE-Mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

Sweaterdog commited on Mar 19

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b7ea651

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1 Parent(s): c0296ec

Update README.md

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@@ -10,7 +10,7 @@ library_name: transformers
 _The **C**ode **R**easoning **E**pert (for) **P**roject **E**xploration_
-# The GRaPE Family
 | Attribute | Size | Modalities | Domain |
 | :--- | :--- | :--- | :--- |
 | **CRePE Mini** | 3B | Text + Image + Video in, Text out | FIM / Autocomplete |
@@ -39,7 +39,7 @@ I recommend using **LM Studio** for running GRaPE / CRePE Models, and have gener
 ***
-# GRaPE Mini as a Model
 CRePE Mini is more an experiment than anything. This model was trained on The Trellis dataset for code samples, and all code examples from the GRaPE Instruct dataset. And thus has become an apt coder for light tasks. It is in no way designed to replace coders, only to empower them.

 _The **C**ode **R**easoning **E**pert (for) **P**roject **E**xploration_
+# The Model
 | Attribute | Size | Modalities | Domain |
 | :--- | :--- | :--- | :--- |
 | **CRePE Mini** | 3B | Text + Image + Video in, Text out | FIM / Autocomplete |
 ***
+# CRePE Mini as a Model
 CRePE Mini is more an experiment than anything. This model was trained on The Trellis dataset for code samples, and all code examples from the GRaPE Instruct dataset. And thus has become an apt coder for light tasks. It is in no way designed to replace coders, only to empower them.