Downloads · 30 days
524
14% of all-time downloads
mradermacher/LocalAI-functioncall-phi-4-v0.2-GGUF
LocalAI-functioncall-phi-4-v0.2-GGUF is a machine learning model from mradermacher. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
static quants of https://huggingface.co/LocalAI-io/LocalAI-functioncall-phi-4-v0.2
Downloads · 30 days
524
14% of all-time downloads
All-time downloads
3.8K
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101 GB
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2
Public
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.gguf101 GB · 100%
From the Hugging Face model README
static quants of https://huggingface.co/LocalAI-io/LocalAI-functioncall-phi-4-v0.2
<!-- provided-files -->For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants are available at https://huggingface.co/mradermacher/LocalAI-functioncall-phi-4-v0.2-i1-GGUF
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|---|---|---|---|
| GGUF | Q2_K | 5.7 | |
| GGUF | Q3_K_S | 6.6 | |
| GGUF | Q3_K_M | 7.3 | lower quality |
| GGUF | Q3_K_L | 7.9 | |
| GGUF | IQ4_XS | 8.2 | |
| GGUF | Q4_K_S | 8.5 | fast, recommended |
| GGUF | Q4_K_M | 9.0 | fast, recommended |
| GGUF | Q5_K_S | 10.3 | |
| GGUF | Q5_K_M | 10.5 | |
| GGUF | Q6_K | 12.1 | very good quality |
| GGUF | Q8_0 | 15.7 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.
I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
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