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

Libraries

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

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

pipe = pipeline("text-generation", model="SL-AI/GRaPE-Mini-Instruct")
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-Instruct")
model = AutoModelForImageTextToText.from_pretrained("SL-AI/GRaPE-Mini-Instruct")
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-Instruct 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-Instruct"
# 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-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

Sweaterdog commited on Mar 19

Commit

eabcff0

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1 Parent(s): ebc2c4a

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@@ -1,3 +1,72 @@
----
-license: apache-2.0
----

+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+---
+![GRaPE_Logo](https://cdn-uploads.huggingface.co/production/uploads/66960602f0ffd8e3a381106a/XjHkzctrE41e1qqJYeDzN.png)
+_The **G**eneral **R**easoning **A**gent (for) **P**roject **E**xploration_
+# The GRaPE Family
+| Attribute | Size | Modalities | Domain |
+| :--- | :--- | :--- | :--- |
+| **GRaPE Flash** | 7B A1B | Text in, Text out | High-Speed Applications |
+| **GRaPE Mini** *(Instruct)* | 3B | Text + Image + Video in, Text out | On-Device Deployment |
+| **GRaPE Nano** | 700M | Text in, Text out | Extreme Edge Deployment |
+***
+# Capabilities
+The GRaPE Family was trained on about **14 billion** tokens of data after pre-training. About half was code related tasks, with the rest being heavy on STEAM. Ensuring the model has a sound logical basis.
+***
+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:
+| Name | Value |
+| :--- | :--- |
+| **Temperature** | 0.6 |
+| **Top K Sampling** | 40 |
+| **Repeat Penalty** | 1 |
+| **Top P Sampling** | 0.85 |
+| **Min P Sampling** | 0.05 |
+# Uses of GRaPE Mini Right Now
+GRaPE Mini was foundational to the existence of [Andy-4.1](https://huggingface.co/Mindcraft-CE/Andy-4.1), a model trained to play Minecraft. This was a demo proving the efficiency and power this architecture can make.
+# GRaPE Mini as a Model
+GRaPE Mini Instruct is a version of GRaPE Mini that **was not** trained on any data regarding reasoning tasks. It was the foundation which allowed for the unique architecture shown in GRaPE Mini to truly be expressed.
+GRaPE Mini Instruct exists also as a way for lower compute devices to run GRaPE Models.
+# Architecture
+* GRaPE Flash: Built on the `OlMoE` Architecture, allowing for incredibly fast speeds where it matters. Allows for retaining factual information, but lacks in logical tasks.
+* GRaPE Mini: Built on the `Qwen3 VL` Architecture, allowing for edge case deployments, where logic cannot be sacrificed.
+* GRaPE Nano: Built on the `LFM 2` Architecture, allowing for the fastest speed, and the most knowledge in the tiniest package.
+***
+# Notes
+The GRaPE Family started all the way back in August of 2025, meaning these models are severely out of date on architecture, and training data.
+GRaPE 2 will come sooner than the GRaPE 1 family had, and will show multiple improvements.
+There are no benchmarks for GRaPE 1 Models due to the costly nature of running them, as well as prioritization of newer models.
+Updates for GRaPE 2 models will be posted here on Huggingface, as well as [Skinnertopia](https://www.skinnertopia.com/)
+Demos for select GRaPE Models can be found here: https://github.com/Sweaterdog/GRaPE-Demos