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Llama-3.2-Taiwan-3B-Instruct-i1-GGUF

Transformers
GGUF
Taiwan
ROC
zh-tw
instruct
chat
llama3.2
SLM
Inference Endpoints
imatrix
conversational
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Llama-3.2-Taiwan-3B-Instruct-i1-GGUF / README.md
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metadata
base_model: DavidLanz/Llama-3.2-Taiwan-3B-Instruct
datasets:
  - DavidLanz/TaiwanChat
  - lianghsun/tw-emergency-medicine-bench
  - lianghsun/tw-legal-nlp
  - lianghsun/tw-legal-synthetic-qa
  - lianghsun/tw-law-article-qa
  - lianghsun/tw-judgment-qa
  - lianghsun/tw-judgment-gist-chat
  - lianghsun/tw-bar-examination-2020-chat
  - lianghsun/tw-structured-law-article
  - lianghsun/tw-judgment-gist-chat
  - lianghsun/tw-contract-review-chat
  - lianghsun/reasoning-base-20k-chat
  - lianghsun/vulnerability-mitigation-qa-zh_tw
  - lianghsun/tw-instruct
  - rombodawg/Everything_Instruct_Multilingual
  - xzuyn/manythings-translations-alpaca
  - neural-bridge/rag-dataset-12000
  - minyichen/glaive_toolcall_zh_tw
language:
  - zh
  - en
  - it
  - de
  - fr
  - ja
  - ko
library_name: transformers
license: llama3.2
quantized_by: mradermacher
tags:
  - Taiwan
  - ROC
  - zh-tw
  - instruct
  - chat
  - llama3.2
  - SLM

About

weighted/imatrix quants of https://huggingface.co/DavidLanz/Llama-3.2-Taiwan-3B-Instruct

static quants are available at https://huggingface.co/mradermacher/Llama-3.2-Taiwan-3B-Instruct-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 i1-IQ1_S 1.0 for the desperate
GGUF i1-IQ1_M 1.0 mostly desperate
GGUF i1-IQ2_XXS 1.1
GGUF i1-IQ2_XS 1.2
GGUF i1-IQ2_S 1.3
GGUF i1-IQ2_M 1.3
GGUF i1-Q2_K_S 1.4 very low quality
GGUF i1-IQ3_XXS 1.4 lower quality
GGUF i1-Q2_K 1.5 IQ3_XXS probably better
GGUF i1-IQ3_XS 1.6
GGUF i1-IQ3_S 1.6 beats Q3_K*
GGUF i1-Q3_K_S 1.6 IQ3_XS probably better
GGUF i1-IQ3_M 1.7
GGUF i1-Q3_K_M 1.8 IQ3_S probably better
GGUF i1-Q3_K_L 1.9 IQ3_M probably better
GGUF i1-IQ4_XS 1.9
GGUF i1-IQ4_NL 2.0 prefer IQ4_XS
GGUF i1-Q4_0 2.0 fast, low quality
GGUF i1-Q4_K_S 2.0 optimal size/speed/quality
GGUF i1-Q4_K_M 2.1 fast, recommended
GGUF i1-Q4_1 2.2
GGUF i1-Q5_K_S 2.4
GGUF i1-Q5_K_M 2.4
GGUF i1-Q6_K 2.7 practically like static Q6_K

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.