---
base_model: CausalLM/35b-beta-long
datasets:
- JosephusCheung/GuanacoDataset
- meta-math/MetaMathQA
- jondurbin/airoboros-3.1
- WizardLM/WizardLM_evol_instruct_V2_196k
- RyokoAI/ShareGPT52K
- RyokoAI/Fandom23K
- milashkaarshif/MoeGirlPedia_wikitext_raw_archive
- wikipedia
- wiki_lingua
- garage-bAInd/Open-Platypus
- LDJnr/Puffin
- BAAI/COIG
- TigerResearch/tigerbot-zhihu-zh-10k
- liwu/MNBVC
- teknium/openhermes
- CausalLM/Refined-Anime-Text
- microsoft/orca-math-word-problems-200k
- m-a-p/CodeFeedback-Filtered-Instruction
language:
- en
- zh
- ja
- de
library_name: transformers
license: wtfpl
quantized_by: mradermacher
---
## About

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static quants of https://huggingface.co/CausalLM/35b-beta-long

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weighted/imatrix quants are available at https://huggingface.co/mradermacher/35b-beta-long-i1-GGUF
## Usage

If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) 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](https://huggingface.co/mradermacher/35b-beta-long-GGUF/resolve/main/35b-beta-long.Q2_K.gguf) | Q2_K | 13.9 |  |
| [GGUF](https://huggingface.co/mradermacher/35b-beta-long-GGUF/resolve/main/35b-beta-long.Q3_K_S.gguf) | Q3_K_S | 16.0 |  |
| [GGUF](https://huggingface.co/mradermacher/35b-beta-long-GGUF/resolve/main/35b-beta-long.Q3_K_M.gguf) | Q3_K_M | 17.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/35b-beta-long-GGUF/resolve/main/35b-beta-long.Q3_K_L.gguf) | Q3_K_L | 19.2 |  |
| [GGUF](https://huggingface.co/mradermacher/35b-beta-long-GGUF/resolve/main/35b-beta-long.IQ4_XS.gguf) | IQ4_XS | 19.4 |  |
| [GGUF](https://huggingface.co/mradermacher/35b-beta-long-GGUF/resolve/main/35b-beta-long.Q4_K_S.gguf) | Q4_K_S | 20.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/35b-beta-long-GGUF/resolve/main/35b-beta-long.Q4_K_M.gguf) | Q4_K_M | 21.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/35b-beta-long-GGUF/resolve/main/35b-beta-long.Q5_K_S.gguf) | Q5_K_S | 24.4 |  |
| [GGUF](https://huggingface.co/mradermacher/35b-beta-long-GGUF/resolve/main/35b-beta-long.Q5_K_M.gguf) | Q5_K_M | 25.1 |  |
| [GGUF](https://huggingface.co/mradermacher/35b-beta-long-GGUF/resolve/main/35b-beta-long.Q6_K.gguf) | Q6_K | 28.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/35b-beta-long-GGUF/resolve/main/35b-beta-long.Q8_0.gguf) | Q8_0 | 37.3 | fast, best quality |

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

![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.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](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.

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