How to use from
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 rhoahndur/retrosynthesis-qwen3-4b-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 rhoahndur/retrosynthesis-qwen3-4b-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rhoahndur/retrosynthesis-qwen3-4b-gguf to start chatting
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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