How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Astral-4B-Preview-GGUF:
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Astral-4B-Preview-GGUF:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Astral-4B-Preview-GGUF:
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Astral-4B-Preview-GGUF:
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 prithivMLmods/Astral-4B-Preview-GGUF:
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Astral-4B-Preview-GGUF:
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 prithivMLmods/Astral-4B-Preview-GGUF:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Astral-4B-Preview-GGUF:
Use Docker
docker model run hf.co/prithivMLmods/Astral-4B-Preview-GGUF:
Quick Links

Astral-4B-Preview-GGUF

Astral-4B-Preview is a reasoning-centric language model from the Astral series, built on Qwen3-4b-thinking-2507 and fine-tuned on the nvidia/AceReason-1.1-SFT dataset to deliver configurable, step-by-step logical reasoning for research and development use. By including a "Reasoning-level" directive in the system prompt, users can control the modelโ€™s depth of reasoningโ€”from direct answers to ultra-detailed reasoning tracesโ€”enabling nuanced, structured responses tailored to diverse problem-solving needs. As a preview release, Astral-4B-Preview is ideal for evaluating advanced reasoning capabilities with user-guided depth control in scientific and technical tasks.

Model Files

File Name Quant Type File Size
Astral-4B-Preview.BF16.gguf BF16 8.05 GB
Astral-4B-Preview.F16.gguf F16 8.05 GB
Astral-4B-Preview.F32.gguf F32 16.1 GB
Astral-4B-Preview.Q2_K.gguf Q2_K 1.67 GB
Astral-4B-Preview.Q3_K_L.gguf Q3_K_L 2.24 GB
Astral-4B-Preview.Q3_K_M.gguf Q3_K_M 2.08 GB
Astral-4B-Preview.Q3_K_S.gguf Q3_K_S 1.89 GB
Astral-4B-Preview.Q4_K_M.gguf Q4_K_M 2.5 GB
Astral-4B-Preview.Q4_K_S.gguf Q4_K_S 2.38 GB
Astral-4B-Preview.Q5_K_M.gguf Q5_K_M 2.89 GB
Astral-4B-Preview.Q5_K_S.gguf Q5_K_S 2.82 GB
Astral-4B-Preview.Q6_K.gguf Q6_K 3.31 GB
Astral-4B-Preview.Q8_0.gguf Q8_0 4.28 GB

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

image.png

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GGUF
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4B params
Architecture
qwen3
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