How to use from
llama.cpp
# Gated model: Login with a HF token with gated access permission
hf auth login
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
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Dolphin3.0 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 4096 context (1B degrades past ~4k; raise only if you have RAM headroom)
  • -ngl 99 offload 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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