---
base_model: AbacusResearch/Jallabi-34B
language:
- en
library_name: transformers
license: apache-2.0
no_imatrix: nan1
quantized_by: mradermacher
tags:
- llama
- LlamaForCausalLM
- LLM
---
## About

<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type:  -->
<!-- ### tags:  -->
static quants of https://huggingface.co/AbacusResearch/Jallabi-34B

<!-- provided-files -->
## 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/Jallabi-34B-GGUF/resolve/main/Jallabi-34B.Q2_K.gguf) | Q2_K | 12.9 |  |
| [GGUF](https://huggingface.co/mradermacher/Jallabi-34B-GGUF/resolve/main/Jallabi-34B.IQ3_XS.gguf) | IQ3_XS | 14.3 |  |
| [GGUF](https://huggingface.co/mradermacher/Jallabi-34B-GGUF/resolve/main/Jallabi-34B.Q3_K_S.gguf) | Q3_K_S | 15.1 |  |
| [GGUF](https://huggingface.co/mradermacher/Jallabi-34B-GGUF/resolve/main/Jallabi-34B.IQ3_S.gguf) | IQ3_S | 15.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Jallabi-34B-GGUF/resolve/main/Jallabi-34B.IQ3_M.gguf) | IQ3_M | 15.7 |  |
| [GGUF](https://huggingface.co/mradermacher/Jallabi-34B-GGUF/resolve/main/Jallabi-34B.Q3_K_M.gguf) | Q3_K_M | 16.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Jallabi-34B-GGUF/resolve/main/Jallabi-34B.Q3_K_L.gguf) | Q3_K_L | 18.2 |  |
| [GGUF](https://huggingface.co/mradermacher/Jallabi-34B-GGUF/resolve/main/Jallabi-34B.IQ4_XS.gguf) | IQ4_XS | 18.7 |  |
| [GGUF](https://huggingface.co/mradermacher/Jallabi-34B-GGUF/resolve/main/Jallabi-34B.Q4_K_S.gguf) | Q4_K_S | 19.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Jallabi-34B-GGUF/resolve/main/Jallabi-34B.Q4_K_M.gguf) | Q4_K_M | 20.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Jallabi-34B-GGUF/resolve/main/Jallabi-34B.Q5_K_S.gguf) | Q5_K_S | 23.8 |  |
| [GGUF](https://huggingface.co/mradermacher/Jallabi-34B-GGUF/resolve/main/Jallabi-34B.Q5_K_M.gguf) | Q5_K_M | 24.4 |  |
| [GGUF](https://huggingface.co/mradermacher/Jallabi-34B-GGUF/resolve/main/Jallabi-34B.Q6_K.gguf) | Q6_K | 28.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Jallabi-34B-GGUF/resolve/main/Jallabi-34B.Q8_0.gguf) | Q8_0 | 36.6 | 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.

<!-- end -->