---
base_model: grimulkan/Goliath-longLORA-120b-rope8-32k-fp16
language:
- en
library_name: transformers
license: llama2
no_imatrix: "nan"
quantized_by: mradermacher
---
## About

<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type:  -->
<!-- ### vocab_type:  -->
static quants of https://huggingface.co/grimulkan/Goliath-longLORA-120b-rope8-32k-fp16

<!-- provided-files -->
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](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/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q2_K.gguf) | Q2_K | 43.3 |  |
| [GGUF](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ3_XS.gguf) | IQ3_XS | 48.2 |  |
| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q3_K_S.gguf.part2of2) | Q3_K_S | 50.8 |  |
| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ3_S.gguf.part2of2) | IQ3_S | 51.0 | beats Q3_K* |
| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ3_M.gguf.part2of2) | IQ3_M | 52.7 |  |
| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q3_K_M.gguf.part2of2) | Q3_K_M | 56.7 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q3_K_L.gguf.part2of2) | Q3_K_L | 61.8 |  |
| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ4_XS.gguf.part2of2) | IQ4_XS | 63.5 |  |
| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q4_K_S.gguf.part2of2) | Q4_K_S | 66.9 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q4_K_M.gguf.part2of2) | Q4_K_M | 70.7 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q5_K_S.gguf.part2of2) | Q5_K_S | 81.1 |  |
| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q5_K_M.gguf.part2of2) | Q5_K_M | 83.3 |  |
| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q6_K.gguf.part2of2) | Q6_K | 96.7 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q8_0.gguf.part3of3) | Q8_0 | 125.2 | 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 -->