Instructions to use Cyclone300/WolfGang300 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Cyclone300/WolfGang300 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 Cyclone300/WolfGang300:IQ1_S # Run inference directly in the terminal: llama cli -hf Cyclone300/WolfGang300:IQ1_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Cyclone300/WolfGang300:IQ1_S # Run inference directly in the terminal: llama cli -hf Cyclone300/WolfGang300:IQ1_S
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf Cyclone300/WolfGang300:IQ1_S # Run inference directly in the terminal: ./build/bin/llama cli -hf Cyclone300/WolfGang300:IQ1_S
Use Docker
docker model run hf.co/Cyclone300/WolfGang300:IQ1_S
- Llama new
- LM Studio
- Jan
- Ollama
How to use Cyclone300/WolfGang300 with Ollama:
ollama run hf.co/Cyclone300/WolfGang300:IQ1_S
- Unsloth Desktop
- Docker Model Runner
How to use Cyclone300/WolfGang300 with Docker Model Runner:
docker model run hf.co/Cyclone300/WolfGang300:IQ1_S
- Lemonade
How to use Cyclone300/WolfGang300 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Cyclone300/WolfGang300:IQ1_S
Run and chat with the model
lemonade run user.WolfGang300-IQ1_S
List all available models
lemonade list
- Atomic Chat
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Cyclone300/WolfGang300:IQ1_S# Run inference directly in the terminal:
llama cli -hf Cyclone300/WolfGang300:IQ1_SInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Cyclone300/WolfGang300:IQ1_S# Run inference directly in the terminal:
llama cli -hf Cyclone300/WolfGang300:IQ1_SBuild from source code
git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf Cyclone300/WolfGang300:IQ1_S# Run inference directly in the terminal:
./build/bin/llama cli -hf Cyclone300/WolfGang300:IQ1_SUse Docker
docker model run hf.co/Cyclone300/WolfGang300:IQ1_SDolphin3.0 Llama3.2 1B β GGUF (mobile-optimized)
GGUF quants of dphn/Dolphin3.0-Llama3.2-1B,
tuned for on-device / phone inference. Quantized with the i1 importance matrix
(imatrix) for better quality at low bit widths.
Files
| File | Size | Quant | Notes |
|---|---|---|---|
Dolphin3.0-Llama3.2-1B.i1-IQ1_S.gguf |
394 MB | IQ1_S | Smallest. Lowest memory, lowest quality. |
Dolphin3.0-Llama3.2-1B.i1-Q4_K_M.gguf |
808 MB | Q4_K_M | Recommended. Best coherence per byte. |
Phone usage
Requires a llama.cpp build with a GGUF loader (MLC, Ollama, llama.cpp, ChatterUI, PocketPal, MLC Chat, or an iOS/Android app with llama.cpp support).
llama.cpp CLI:
llama-cli -m Dolphin3.0-Llama3.2-1B.i1-Q4_K_M.gguf \
-ngl 99 -c 4096 -n 512 \
--jinja \
-p "<|im_start|>user
Hi<|im_end|>
<|im_start|>assistant
"
Chat template (ChatML, as used by Dolphin):
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
Recommended phone settings:
-c 4096context (1B degrades past ~4k; raise only if you have RAM headroom)-ngl 99offload everything to GPU/NPU- Prefer Q4_K_M. Drop to IQ1_S only on very low-RAM devices.
Provenance
Quants mirrored from mradermacher/Dolphin3.0-Llama3.2-1B-i1-GGUF.
Base model by the Dolphin project (dphn). No weights were modified in this mirror.
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