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lfm2.5-230m-code-math-GGUF
hauser458b/lfm2.5-230m-code-math-GGUF
lfm2.5-230m-code-math-GGUF is a text generation model from hauser458b. Use it when you need the model to write or continue text. The card lists the license as other.
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…
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Repo size
1.2 GB
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.gguf1.2 GB · 100%
At a glance
- Task
- Text Generation
- License
- other
- Access
- Public
- Created
- Jul 13, 2026
- Updated
- Jul 29, 2026
- SHA
- 38888637
Try a prompt
Base models
Model card
From the Hugging Face model README
LFM2.5-230M-Code-Math-GGUF
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.
- Task
- Text Generation
- License
- other
- Languages
- en
- Created
- Jul 13, 2026
- Updated
- Jul 29, 2026