Instructions to use mradermacher/snapgate-3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/snapgate-3B-GGUF with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="mradermacher/snapgate-3B-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/snapgate-3B-GGUF", dtype="auto") - llama-cpp-python
How to use mradermacher/snapgate-3B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="mradermacher/snapgate-3B-GGUF", filename="snapgate-3B.IQ4_XS.gguf", )
llm.create_chat_completion( messages = "\"The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.\"" )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use mradermacher/snapgate-3B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf mradermacher/snapgate-3B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mradermacher/snapgate-3B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf mradermacher/snapgate-3B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mradermacher/snapgate-3B-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 mradermacher/snapgate-3B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/snapgate-3B-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 mradermacher/snapgate-3B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/snapgate-3B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/snapgate-3B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/snapgate-3B-GGUF with Ollama:
ollama run hf.co/mradermacher/snapgate-3B-GGUF:Q4_K_M
- Unsloth Studio new
How to use mradermacher/snapgate-3B-GGUF 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 mradermacher/snapgate-3B-GGUF 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 mradermacher/snapgate-3B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mradermacher/snapgate-3B-GGUF to start chatting
- Pi new
How to use mradermacher/snapgate-3B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf mradermacher/snapgate-3B-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "mradermacher/snapgate-3B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mradermacher/snapgate-3B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf mradermacher/snapgate-3B-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default mradermacher/snapgate-3B-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use mradermacher/snapgate-3B-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/snapgate-3B-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/snapgate-3B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/snapgate-3B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.snapgate-3B-GGUF-Q4_K_M
List all available models
lemonade list
About
static quants of https://huggingface.co/kadalicious22/snapgate-3B
For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.
Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|---|---|---|---|
| GGUF | Q2_K | 1.4 | |
| GGUF | Q3_K_S | 1.6 | |
| GGUF | Q3_K_M | 1.7 | lower quality |
| GGUF | Q3_K_L | 1.8 | |
| GGUF | IQ4_XS | 1.9 | |
| GGUF | Q4_K_S | 1.9 | fast, recommended |
| GGUF | Q4_K_M | 2.0 | fast, recommended |
| GGUF | Q5_K_S | 2.3 | |
| GGUF | Q5_K_M | 2.3 | |
| GGUF | Q6_K | 2.6 | very good quality |
| GGUF | Q8_0 | 3.4 | fast, best quality |
| GGUF | f16 | 6.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.
Thanks
I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.
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