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

Libraries

How to use Sweaterdog/GRaPE-Mini-Beta-Thinking with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Sweaterdog/GRaPE-Mini-Beta-Thinking",
	filename="GRaPE-mini-beta-thinking.F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Sweaterdog/GRaPE-Mini-Beta-Thinking 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 Sweaterdog/GRaPE-Mini-Beta-Thinking:F16
# Run inference directly in the terminal:
llama cli -hf Sweaterdog/GRaPE-Mini-Beta-Thinking:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Sweaterdog/GRaPE-Mini-Beta-Thinking:F16
# Run inference directly in the terminal:
llama cli -hf Sweaterdog/GRaPE-Mini-Beta-Thinking:F16

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 Sweaterdog/GRaPE-Mini-Beta-Thinking:F16
# Run inference directly in the terminal:
./llama-cli -hf Sweaterdog/GRaPE-Mini-Beta-Thinking:F16

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 Sweaterdog/GRaPE-Mini-Beta-Thinking:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Sweaterdog/GRaPE-Mini-Beta-Thinking:F16

Use Docker

docker model run hf.co/Sweaterdog/GRaPE-Mini-Beta-Thinking:F16

LM Studio
Jan

vLLM

How to use Sweaterdog/GRaPE-Mini-Beta-Thinking with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Sweaterdog/GRaPE-Mini-Beta-Thinking:F16

Ollama
How to use Sweaterdog/GRaPE-Mini-Beta-Thinking with Ollama:
```
ollama run hf.co/Sweaterdog/GRaPE-Mini-Beta-Thinking:F16
```

Unsloth Studio

How to use Sweaterdog/GRaPE-Mini-Beta-Thinking with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Sweaterdog/GRaPE-Mini-Beta-Thinking to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Sweaterdog/GRaPE-Mini-Beta-Thinking to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Sweaterdog/GRaPE-Mini-Beta-Thinking to start chatting

Atomic Chat new
Docker Model Runner
How to use Sweaterdog/GRaPE-Mini-Beta-Thinking with Docker Model Runner:
```
docker model run hf.co/Sweaterdog/GRaPE-Mini-Beta-Thinking:F16
```

Lemonade

How to use Sweaterdog/GRaPE-Mini-Beta-Thinking with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Sweaterdog/GRaPE-Mini-Beta-Thinking:F16

Run and chat with the model

lemonade run user.GRaPE-Mini-Beta-Thinking-F16

List all available models

lemonade list

Sweaterdog commited on Oct 15, 2025

Commit

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@@ -57,14 +57,10 @@ Along with GRaPE Mini, a 7B MoE (Mixture of Experts) model based from OlMoE will
 ## Capabilties of GRaPE Mini
 GRaPE was trained to be a coding assistant, and to excel in STEM topics. The model *may* falter on historical information, or factual information due to the low parameter size.
-A demo of a website it had generated for itself can be found [here](GRaPE_Mini_Beta.html).
 ***
-## 🚀 Benchmarks
-**BENCHMARKS COMING SOON**
 ## 🧠 Model Philosophy: The Art of the Finetune
 While GRaPE Mini is not trained "from-scratch" (i.e., from random weights), it represents an extensive and highly curated instruction-tuning process. A base model possesses linguistic structure but lacks the ability to follow instructions, reason, or converse. The true "creation" of an assistant like GRaPE lies in the meticulous selection, blending, and application of high-quality datasets. This finetuning process is what transforms a raw linguistic engine into a capable and helpful agent.

 ## Capabilties of GRaPE Mini
 GRaPE was trained to be a coding assistant, and to excel in STEM topics. The model *may* falter on historical information, or factual information due to the low parameter size.
+A demo of a website it had generated for itself can be found [here](https://huggingface.co/Sweaterdog/GRaPE-Mini-Beta/blob/main/GRaPE_Mini_Beta.html).
 ***
 ## 🧠 Model Philosophy: The Art of the Finetune
 While GRaPE Mini is not trained "from-scratch" (i.e., from random weights), it represents an extensive and highly curated instruction-tuning process. A base model possesses linguistic structure but lacks the ability to follow instructions, reason, or converse. The true "creation" of an assistant like GRaPE lies in the meticulous selection, blending, and application of high-quality datasets. This finetuning process is what transforms a raw linguistic engine into a capable and helpful agent.