README.md

metadata

base_model: kshitijthakkar/loggenix-moe-0.3B-A0.1B-e3-lr7e5-b16-4090-v5.1-finetuned
datasets:
  - kshitijthakkar/loggenix-mc-oraca-agentinstruct-1m-v1
language:
  - en
library_name: transformers
license: apache-2.0
mradermacher:
  readme_rev: 1
quantized_by: mradermacher
tags:
  - qwen3
  - qwen3moe
  - mixture-of-experts
  - llm
  - text-generation
  - instruction-following
  - agentic-ai
  - tool-use
  - low-resource
  - edge-ai
  - from-scratch
  - causal-lm

About

static quants of https://huggingface.co/kshitijthakkar/loggenix-moe-0.3B-A0.1B-e3-lr7e5-b16-4090-v5.1-finetuned

For a convenient overview and download list, visit our model page for this model.

weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.

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	0.3
GGUF	Q3_K_S	0.3
GGUF	Q3_K_M	0.3	lower quality
GGUF	Q3_K_L	0.3
GGUF	IQ4_XS	0.3
GGUF	Q4_K_S	0.3	fast, recommended
GGUF	Q4_K_M	0.3	fast, recommended
GGUF	Q5_K_S	0.3
GGUF	Q5_K_M	0.3
GGUF	Q6_K	0.4	very good quality
GGUF	Q8_0	0.4	fast, best quality
GGUF	f16	0.7	16 bpw, overkill

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

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.