How to use from
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 SpermAI/SpermLLM-S1-Qwen3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SpermAI/SpermLLM-S1-Qwen3-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 SpermAI/SpermLLM-S1-Qwen3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SpermAI/SpermLLM-S1-Qwen3-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 SpermAI/SpermLLM-S1-Qwen3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SpermAI/SpermLLM-S1-Qwen3-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 SpermAI/SpermLLM-S1-Qwen3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SpermAI/SpermLLM-S1-Qwen3-GGUF:Q4_K_M
Use Docker
docker model run hf.co/SpermAI/SpermLLM-S1-Qwen3-GGUF:Q4_K_M
Quick Links

๐Ÿง  SpermLLM โ€” Distilled Reasoning Model

Parameters Method Format License

SpermLLM is a compact distilled reasoning model based on Qwen3-0.6B-Instruct, designed to improve performance in math, coding, and structured reasoning while remaining lightweight and efficient.

Training Method

The model was fine-tuned on a mixture of curated instruction datasets and further distilled from larger teacher models (Mix of GPT-OSS-120B and Kimi K2.5)

Training Overview

  • Base Model: Qwen3 0.6B Instruct
  • Training Method: SFT (Supervised Finetuning) + Distillation

Notes

SpermLLM is an experimental model, We plan on making this larger and better! Currently no benchmarks but benchmarks will be soon! (Never)

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GGUF
Model size
0.8B params
Architecture
qwen3
Hardware compatibility
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4-bit

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