GGUF quantized versions of hauser458b/lfm2.5-230m-code-math, a code/math-focused fine-tune of LiquidAI/LFM2.5-230M (instruct). See the base fine-tune's model card for full training details, evaluation notes, and known limitations.

For use with llama.cpp, Ollama, LM Studio, or any other GGUF-compatible runtime.

Files

File	Quantization	Approx. size	Notes
`lfm2.5-230m-code-math-F16.gguf`	F16	~460 MB	Full precision, largest, highest fidelity
`lfm2.5-230m-code-math-Q8_0.gguf`	Q8_0	~245 MB	Near-lossless, good default if size isn't a concern
`lfm2.5-230m-code-math-Q5_K_M.gguf`	Q5_K_M	~165 MB	Good balance of size/quality
`lfm2.5-230m-code-math-Q5_K_S.gguf`	Q5_K_S	~155 MB	Slightly smaller than Q5_K_M, marginal quality trade-off
`lfm2.5-230m-code-math-Q4_K_M.gguf`	Q4_K_M	~135 MB	Smallest here, most aggressive quantization, best for constrained/edge devices

(Sizes are approximate — check actual file sizes in the repo.)

Usage

llama.cpp

./llama-cli -m lfm2.5-230m-code-math-Q5_K_M.gguf -p "Write a Python function to check if a number is prime."

Ollama

ollama run hf.co/hauser458b/lfm2.5-230m-code-math-GGUF:Q5_K_M

LM Studio

Search for hauser458b/lfm2.5-230m-code-math-GGUF in the LM Studio model browser, or download a .gguf file directly and load it manually.

Which quant should I use?

Q4_K_M: smallest footprint, best for very constrained devices (older phones, low-RAM edge hardware). Some quality loss vs. higher quants.
Q5_K_S / Q5_K_M: good middle ground — recommended default for most laptop/desktop CPU inference.
Q8_0: near-lossless, use if you have the RAM/storage headroom and want output as close as possible to the original safetensors model.
F16: full precision GGUF, only needed if you plan to re-quantize yourself or want the highest possible fidelity in llama.cpp.

License

Inherits the LFM Open License v1.0 from the base model.