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 rhoahndur/retrosynthesis-qwen3-4b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rhoahndur/retrosynthesis-qwen3-4b-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 rhoahndur/retrosynthesis-qwen3-4b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rhoahndur/retrosynthesis-qwen3-4b-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 rhoahndur/retrosynthesis-qwen3-4b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf rhoahndur/retrosynthesis-qwen3-4b-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 rhoahndur/retrosynthesis-qwen3-4b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rhoahndur/retrosynthesis-qwen3-4b-gguf:Q4_K_M
Use Docker
docker model run hf.co/rhoahndur/retrosynthesis-qwen3-4b-gguf:Q4_K_M
Quick Links

Retrosynthesis Qwen3-4B GGUF

Q4_K_M quantized version of rhoahndur/retrosynthesis-qwen3-4b for CPU inference via llama.cpp.

Size: 2.5 GB Quantization: Q4_K_M

Usage with llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="rhoahndur/retrosynthesis-qwen3-4b-gguf",
    filename="retrosynthesis-qwen3-4b-Q4_K_M.gguf",
    n_ctx=512,
)
output = llm.create_chat_completion(
    messages=[
        {"role": "system", "content": "You are a retrosynthesis expert. Output ONLY reactant SMILES separated by dots."},
        {"role": "user", "content": "Predict the reactants for: CC(=O)Oc1ccccc1C(=O)O"}
    ],
    max_tokens=256,
    temperature=0.7,
)
print(output["choices"][0]["message"]["content"])

Demo

Retrosynthesis AI

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GGUF
Model size
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Architecture
qwen3
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