mradermacher/OneLLM-Doey-ChatQA-V1-Llama-3.2-1B-GGUF

---

base_model: DoeyLLM/OneLLM-Doey-ChatQA-V1-Llama-3.2-1B datasets:

• nvidia/ChatQA-Training-Data

language:

• en

library_name: transformers license: apache-2.0 quantized_by: mradermacher

---

About

static quants of https://huggingface.co/DoeyLLM/OneLLM-Doey-ChatQA-V1-Llama-3.2-1B

weighted/imatrix quants are available at https://huggingface.co/mradermacher/OneLLM-Doey-ChatQA-V1-Llama-3.2-1B-i1-GGUF

Usage

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.

Provided Quants

(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	0.7
GGUF	Q3K_S	0.7
GGUF	Q3K_M	0.8	lower quality
GGUF	Q3K_L	0.8
GGUF	IQ4_XS	0.8
GGUF	Q404_4	0.9	fast on arm, low quality
GGUF	Q4K_S	0.9	fast, recommended
GGUF	Q4K_M	0.9	fast, recommended
GGUF	Q5K_S	1.0
GGUF	Q5K_M	1.0
GGUF	Q6_K	1.1	very good quality
GGUF	Q8_0	1.4	fast, best quality
GGUF	f16	2.6	16 bpw, overkill

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

!image.png

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.

Thanks

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.