Fireplace-34b-GGUF / README.md
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metadata
base_model: ValiantLabs/Fireplace-34b
language:
  - en
library_name: transformers
license: other
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
license_name: yi-license
model_type: llama
quantized_by: mradermacher
tags:
  - fireplace
  - function-calling
  - code
  - code-instruct
  - conversational
  - text-generation-inference
  - valiant
  - valiant-labs
  - smaug
  - yi
  - yi-34b
  - llama
  - llama-2
  - llama-2-chat
  - 34b

About

static quants of https://huggingface.co/ValiantLabs/Fireplace-34b

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 14.4
GGUF IQ3_XS 15.8
GGUF Q3_K_S 16.5
GGUF IQ3_S 16.6 beats Q3_K*
GGUF IQ3_M 17.1
GGUF Q3_K_M 18.2 lower quality
GGUF Q3_K_L 19.7
GGUF IQ4_XS 20.2
GGUF Q4_K_S 21.2 fast, recommended
GGUF Q4_K_M 22.2 fast, recommended
GGUF Q5_K_S 25.3
GGUF Q5_K_M 25.9
GGUF Q6_K 29.8 very good quality
GGUF Q8_0 38.0 fast, best quality

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.