Instructions to use SL-AI/GRaPE-2.5-Helios-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SL-AI/GRaPE-2.5-Helios-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="SL-AI/GRaPE-2.5-Helios-GGUF")
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 AutoModel
model = AutoModel.from_pretrained("SL-AI/GRaPE-2.5-Helios-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SL-AI/GRaPE-2.5-Helios-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SL-AI/GRaPE-2.5-Helios-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SL-AI/GRaPE-2.5-Helios-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SL-AI/GRaPE-2.5-Helios-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SL-AI/GRaPE-2.5-Helios-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf SL-AI/GRaPE-2.5-Helios-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SL-AI/GRaPE-2.5-Helios-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf SL-AI/GRaPE-2.5-Helios-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SL-AI/GRaPE-2.5-Helios-GGUF:Q4_K_M

Use Docker

docker model run hf.co/SL-AI/GRaPE-2.5-Helios-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use SL-AI/GRaPE-2.5-Helios-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SL-AI/GRaPE-2.5-Helios-GGUF"
# 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-2.5-Helios-GGUF",
		"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-2.5-Helios-GGUF:Q4_K_M

SGLang

How to use SL-AI/GRaPE-2.5-Helios-GGUF 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-2.5-Helios-GGUF" \
    --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-2.5-Helios-GGUF",
		"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-2.5-Helios-GGUF" \
        --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-2.5-Helios-GGUF",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use SL-AI/GRaPE-2.5-Helios-GGUF with Ollama:
```
ollama run hf.co/SL-AI/GRaPE-2.5-Helios-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use SL-AI/GRaPE-2.5-Helios-GGUF with Docker Model Runner:
```
docker model run hf.co/SL-AI/GRaPE-2.5-Helios-GGUF:Q4_K_M
```

Lemonade

How to use SL-AI/GRaPE-2.5-Helios-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SL-AI/GRaPE-2.5-Helios-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.GRaPE-2.5-Helios-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

This model is insane.

by Koitenshin - opened 17 days ago

Discussion

Koitenshin

17 days ago

And I mean that literally.

I've had it argue with me over code comments. I hadn't changed the comment to 'Nth to last' when I was using a different number than the comment said.

I've seen it act like an office worker stuck in middle management trying to justify its job (i.e., change for the sake of change).

And I've even gotten it to say, in its thinking process, something similar to "This is an absurd amount of work".

I feel like you captured a genuine part of the human condition with this model, well done.

I'm using the Q8_0 GGUF for those wondering.

Sweaterdog

Skinnertopia Lab for Artifical Intelligence org 16 days ago

Thank you so much! That was a big goal with GRaPE 2.5, make it more fun to use than "just get it done Claudius."

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