base_model: abacusai/Smaug-34B-v0.1
exported_from: 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 | Q3_K_S | 16.5 | |
| GGUF | IQ3_S | 16.6 | beats Q3_K* |
| GGUF | Q3_K_M | 18.2 | lower quality |
| GGUF | Q3_K_L | 19.7 | |
| GGUF | Q4_K_S | 21.2 | fast, recommended |
| 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):
And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
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.