Instructions to use SL-AI/GRaPE-2-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SL-AI/GRaPE-2-Flash with Transformers:

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

pipe = pipeline("image-text-to-text", model="SL-AI/GRaPE-2-Flash")
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-2-Flash")
model = AutoModelForImageTextToText.from_pretrained("SL-AI/GRaPE-2-Flash")
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/GRaPE-2-Flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SL-AI/GRaPE-2-Flash"
# 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-2-Flash",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/SL-AI/GRaPE-2-Flash

SGLang

How to use SL-AI/GRaPE-2-Flash 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-2-Flash" \
    --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-2-Flash",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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-2-Flash" \
        --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-2-Flash",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use SL-AI/GRaPE-2-Flash with Docker Model Runner:
```
docker model run hf.co/SL-AI/GRaPE-2-Flash
```

Sweaterdog commited on Apr 19

Commit

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Update README.md

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README.md +4 -51

README.md CHANGED Viewed

@@ -1,7 +1,7 @@
 ---
 license: apache-2.0
 language:
-- en
 - zh
 - fr
 - de
@@ -28,6 +28,7 @@ tags:
 - coding
 - math
 - science
 ---
 ![grape_2_banner](https://cdn-uploads.huggingface.co/production/uploads/66960602f0ffd8e3a381106a/XqhlL-CCTeRgPKDbqyyT7.png)
@@ -37,7 +38,7 @@ _The **G**eneral **R**easoning **A**gent (for) **P**roject **E**xploration_
 | Model | Size | Modalities | Domain |
 | :--- | :--- | :--- | :--- |
-| **GRaPE 2 Pro** | TBA | Image + Text in, Text out | Large-Scale Intelligence and "Raw Reasoning" |
 | **GRaPE 2 Flash** | 9B | Image + Text in, Text out | Advanced Device Deployment |
 | **GRaPE 2 Mini** | 5B | Image + Text in, Text out | On-Device Deployment |
@@ -100,55 +101,7 @@ Implement a red-black tree in Python with insertion and deletion. <thinking_mode
 # Benchmarks
-Scores sourced from official technical reports (Qwen3 Technical Report, May 2025; Qwen2.5 Technical Report, January 2025).
-> **Note:** *Benchmarks are Underway for GRaPE 2 Mini, they will be empty and set as "TBD" for the time being*
-### General Knowledge — MMLU (5-shot)
-| Model | Params | MMLU |
-| :--- | :--- | :--- |
-| **GRaPE 2 Mini** | **5B** | **TBD** |
-| Qwen3-4B-Instruct | 4B | 83.7\* |
-| Qwen3-8B-Instruct | 8B | ~85.0 |
-| Qwen2.5-7B-Instruct | 7B | 74.2 |
-| Gemma-3-12B | 12B | 73.9 |
-| Qwen2.5-14B | 14B | 79.7 |
-### Mathematics — MATH (4-shot)
-| Model | Params | MATH |
-| :--- | :--- | :--- |
-| **GRaPE 2 Mini** | **5B** | **TBD** |
-| Qwen3-4B (Thinking) | 4B | 54.1 |
-| Qwen3-8B (Thinking) | 8B | ~65.0 |
-| Qwen2.5-7B-Instruct | 7B | 75.5 |
-| Qwen2.5-14B | 14B | 55.6 |
-| Gemma-3-12B | 12B | 44.4 |
-### Coding — EvalPlus (avg. HumanEval + MBPP)
-| Model | Params | EvalPlus |
-| :--- | :--- | :--- |
-| **GRaPE 2 Mini** | **5B** | **TBD** |
-| Qwen3-4B-Instruct | 4B | 72.1 |
-| Qwen3-8B-Instruct | 8B | ~76.0 |
-| Qwen2.5-7B-Instruct | 7B | ~65.0 |
-| Gemma-3-12B | 12B | 52.7 |
-| Qwen2.5-14B | 14B | 60.7 |
-### Math Word Problems — GSM8K (4-shot)
-| Model | Params | GSM8K |
-| :--- | :--- | :--- |
-| **GRaPE 2 Mini** | **5B** | **TBD** |
-| Qwen3-4B (Thinking) | 4B | 87.8 |
-| Qwen2.5-7B-Instruct | 7B | 91.1 |
-| Qwen2.5-14B | 14B | 90.2 |
-| Gemma-3-12B | 12B | 78.0 |
-***
 # Recommended Inference Settings

 ---
 license: apache-2.0
 language:
+- en
 - zh
 - fr
 - de
 - coding
 - math
 - science
+new_version: SL-AI/GRaPE-2.1-Flash
 ---
 ![grape_2_banner](https://cdn-uploads.huggingface.co/production/uploads/66960602f0ffd8e3a381106a/XqhlL-CCTeRgPKDbqyyT7.png)
 | Model | Size | Modalities | Domain |
 | :--- | :--- | :--- | :--- |
+| **GRaPE 2 Pro** | 27B | Image + Text in, Text out | Large-Scale Intelligence and "Raw Reasoning" |
 | **GRaPE 2 Flash** | 9B | Image + Text in, Text out | Advanced Device Deployment |
 | **GRaPE 2 Mini** | 5B | Image + Text in, Text out | On-Device Deployment |
 # Benchmarks
+Benchmarks for GRaPE 2 Flash will not exist due to GRaPE 2.1's release.
 # Recommended Inference Settings