modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
mradermacher/Brunhilde-2x7b-MOE-DPO-v.01.5-GGUF | mradermacher | 2024-05-06T05:20:30Z | 38 | 0 | transformers | [
"transformers",
"gguf",
"moe",
"en",
"base_model:arlineka/Brunhilde-2x7b-MOE-DPO-v.01.5",
"base_model:quantized:arlineka/Brunhilde-2x7b-MOE-DPO-v.01.5",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-03T15:02:34Z | ---
base_model: arlineka/Brunhilde-2x7b-MOE-DPO-v.01.5
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- moe
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/arlineka/Brunhilde-2x7b-MOE-DPO-v.01.5
<!-- 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/Brunhilde-2x7b-MOE-DPO-v.01.5-GGUF/resolve/main/Brunhilde-2x7b-MOE-DPO-v.01.5.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Brunhilde-2x7b-MOE-DPO-v.01.5-GGUF/resolve/main/Brunhilde-2x7b-MOE-DPO-v.01.5.IQ3_XS.gguf) | IQ3_XS | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/Brunhilde-2x7b-MOE-DPO-v.01.5-GGUF/resolve/main/Brunhilde-2x7b-MOE-DPO-v.01.5.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Brunhilde-2x7b-MOE-DPO-v.01.5-GGUF/resolve/main/Brunhilde-2x7b-MOE-DPO-v.01.5.IQ3_S.gguf) | IQ3_S | 5.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Brunhilde-2x7b-MOE-DPO-v.01.5-GGUF/resolve/main/Brunhilde-2x7b-MOE-DPO-v.01.5.IQ3_M.gguf) | IQ3_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/Brunhilde-2x7b-MOE-DPO-v.01.5-GGUF/resolve/main/Brunhilde-2x7b-MOE-DPO-v.01.5.Q3_K_M.gguf) | Q3_K_M | 6.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Brunhilde-2x7b-MOE-DPO-v.01.5-GGUF/resolve/main/Brunhilde-2x7b-MOE-DPO-v.01.5.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/Brunhilde-2x7b-MOE-DPO-v.01.5-GGUF/resolve/main/Brunhilde-2x7b-MOE-DPO-v.01.5.IQ4_XS.gguf) | IQ4_XS | 7.2 | |
| [GGUF](https://huggingface.co/mradermacher/Brunhilde-2x7b-MOE-DPO-v.01.5-GGUF/resolve/main/Brunhilde-2x7b-MOE-DPO-v.01.5.Q4_K_S.gguf) | Q4_K_S | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Brunhilde-2x7b-MOE-DPO-v.01.5-GGUF/resolve/main/Brunhilde-2x7b-MOE-DPO-v.01.5.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Brunhilde-2x7b-MOE-DPO-v.01.5-GGUF/resolve/main/Brunhilde-2x7b-MOE-DPO-v.01.5.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/Brunhilde-2x7b-MOE-DPO-v.01.5-GGUF/resolve/main/Brunhilde-2x7b-MOE-DPO-v.01.5.Q5_K_M.gguf) | Q5_K_M | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/Brunhilde-2x7b-MOE-DPO-v.01.5-GGUF/resolve/main/Brunhilde-2x7b-MOE-DPO-v.01.5.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Brunhilde-2x7b-MOE-DPO-v.01.5-GGUF/resolve/main/Brunhilde-2x7b-MOE-DPO-v.01.5.Q8_0.gguf) | Q8_0 | 13.9 | 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
## 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 -->
|
mradermacher/HeatherSpell-7b-GGUF | mradermacher | 2024-05-06T05:20:25Z | 34 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"yam-peleg/Experiment26-7B",
"Kukedlc/NeuralExperiment-7b-MagicCoder-v7.5",
"en",
"base_model:MysticFoxMagic/HeatherSpell-7b",
"base_model:quantized:MysticFoxMagic/HeatherSpell-7b",
"endpoints_compatible",
"region:us"
] | null | 2024-04-03T16:44:58Z | ---
base_model: MysticFoxMagic/HeatherSpell-7b
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- yam-peleg/Experiment26-7B
- Kukedlc/NeuralExperiment-7b-MagicCoder-v7.5
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/MysticFoxMagic/HeatherSpell-7b
<!-- 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/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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 -->
|
mradermacher/pandafish-dt-7b-GGUF | mradermacher | 2024-05-06T05:20:10Z | 64 | 1 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"CultriX/MergeCeption-7B-v3",
"en",
"base_model:ichigoberry/pandafish-dt-7b",
"base_model:quantized:ichigoberry/pandafish-dt-7b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-03T19:03:45Z | ---
base_model: ichigoberry/pandafish-dt-7b
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- CultriX/MergeCeption-7B-v3
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ichigoberry/pandafish-dt-7b
<!-- 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/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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 -->
|
mradermacher/airoboros-l2-70b-2.2-GGUF | mradermacher | 2024-05-06T05:19:59Z | 15 | 0 | transformers | [
"transformers",
"gguf",
"en",
"dataset:jondurbin/airoboros-2.2",
"base_model:jondurbin/airoboros-l2-70b-2.2",
"base_model:quantized:jondurbin/airoboros-l2-70b-2.2",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-03T20:44:45Z | ---
base_model: jondurbin/airoboros-l2-70b-2.2
datasets:
- jondurbin/airoboros-2.2
language:
- en
library_name: transformers
license: llama2
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/jondurbin/airoboros-l2-70b-2.2
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-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/airoboros-l2-70b-2.2-GGUF/resolve/main/airoboros-l2-70b-2.2.Q2_K.gguf) | Q2_K | 25.9 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-GGUF/resolve/main/airoboros-l2-70b-2.2.IQ3_XS.gguf) | IQ3_XS | 28.7 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-GGUF/resolve/main/airoboros-l2-70b-2.2.IQ3_S.gguf) | IQ3_S | 30.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-GGUF/resolve/main/airoboros-l2-70b-2.2.Q3_K_S.gguf) | Q3_K_S | 30.3 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-GGUF/resolve/main/airoboros-l2-70b-2.2.IQ3_M.gguf) | IQ3_M | 31.4 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-GGUF/resolve/main/airoboros-l2-70b-2.2.Q3_K_M.gguf) | Q3_K_M | 33.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-GGUF/resolve/main/airoboros-l2-70b-2.2.Q3_K_L.gguf) | Q3_K_L | 36.6 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-GGUF/resolve/main/airoboros-l2-70b-2.2.IQ4_XS.gguf) | IQ4_XS | 37.6 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-GGUF/resolve/main/airoboros-l2-70b-2.2.Q4_K_S.gguf) | Q4_K_S | 39.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-GGUF/resolve/main/airoboros-l2-70b-2.2.Q4_K_M.gguf) | Q4_K_M | 41.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-GGUF/resolve/main/airoboros-l2-70b-2.2.Q5_K_S.gguf) | Q5_K_S | 47.9 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-GGUF/resolve/main/airoboros-l2-70b-2.2.Q5_K_M.gguf) | Q5_K_M | 49.2 | |
| [PART 1](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-GGUF/resolve/main/airoboros-l2-70b-2.2.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-GGUF/resolve/main/airoboros-l2-70b-2.2.Q6_K.gguf.part2of2) | Q6_K | 57.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-GGUF/resolve/main/airoboros-l2-70b-2.2.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-GGUF/resolve/main/airoboros-l2-70b-2.2.Q8_0.gguf.part2of2) | Q8_0 | 73.6 | 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
## 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 -->
|
mradermacher/FNCARL-7b-GGUF | mradermacher | 2024-05-06T05:19:57Z | 11 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:jambroz/FNCARL-7b",
"base_model:quantized:jambroz/FNCARL-7b",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-03T20:47:28Z | ---
base_model: jambroz/FNCARL-7b
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/jambroz/FNCARL-7b
<!-- 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/FNCARL-7b-GGUF/resolve/main/FNCARL-7b.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-GGUF/resolve/main/FNCARL-7b.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-GGUF/resolve/main/FNCARL-7b.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-GGUF/resolve/main/FNCARL-7b.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-GGUF/resolve/main/FNCARL-7b.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-GGUF/resolve/main/FNCARL-7b.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-GGUF/resolve/main/FNCARL-7b.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-GGUF/resolve/main/FNCARL-7b.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-GGUF/resolve/main/FNCARL-7b.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-GGUF/resolve/main/FNCARL-7b.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-GGUF/resolve/main/FNCARL-7b.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-GGUF/resolve/main/FNCARL-7b.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-GGUF/resolve/main/FNCARL-7b.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-GGUF/resolve/main/FNCARL-7b.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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 -->
|
mradermacher/KunoichiVerse-7B-GGUF | mradermacher | 2024-05-06T05:19:51Z | 28 | 1 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"en",
"base_model:Ppoyaa/KunoichiVerse-7B",
"base_model:quantized:Ppoyaa/KunoichiVerse-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-03T21:48:59Z | ---
base_model: Ppoyaa/KunoichiVerse-7B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Ppoyaa/KunoichiVerse-7B
<!-- 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/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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 -->
|
mradermacher/StarMonarch-7B-GGUF | mradermacher | 2024-05-06T05:19:34Z | 71 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"en",
"base_model:Ppoyaa/StarMonarch-7B",
"base_model:quantized:Ppoyaa/StarMonarch-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-04T00:13:47Z | ---
base_model: Ppoyaa/StarMonarch-7B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Ppoyaa/StarMonarch-7B
<!-- 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/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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 -->
|
mradermacher/StarlingMaid-2x7B-base-GGUF | mradermacher | 2024-05-06T05:19:27Z | 42 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:dawn17/StarlingMaid-2x7B-base",
"base_model:quantized:dawn17/StarlingMaid-2x7B-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-04T00:42:41Z | ---
base_model: dawn17/StarlingMaid-2x7B-base
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/dawn17/StarlingMaid-2x7B-base
<!-- 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/StarlingMaid-2x7B-base-GGUF/resolve/main/StarlingMaid-2x7B-base.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/StarlingMaid-2x7B-base-GGUF/resolve/main/StarlingMaid-2x7B-base.IQ3_XS.gguf) | IQ3_XS | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/StarlingMaid-2x7B-base-GGUF/resolve/main/StarlingMaid-2x7B-base.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/StarlingMaid-2x7B-base-GGUF/resolve/main/StarlingMaid-2x7B-base.IQ3_S.gguf) | IQ3_S | 5.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/StarlingMaid-2x7B-base-GGUF/resolve/main/StarlingMaid-2x7B-base.IQ3_M.gguf) | IQ3_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/StarlingMaid-2x7B-base-GGUF/resolve/main/StarlingMaid-2x7B-base.Q3_K_M.gguf) | Q3_K_M | 6.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/StarlingMaid-2x7B-base-GGUF/resolve/main/StarlingMaid-2x7B-base.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/StarlingMaid-2x7B-base-GGUF/resolve/main/StarlingMaid-2x7B-base.IQ4_XS.gguf) | IQ4_XS | 7.2 | |
| [GGUF](https://huggingface.co/mradermacher/StarlingMaid-2x7B-base-GGUF/resolve/main/StarlingMaid-2x7B-base.Q4_K_S.gguf) | Q4_K_S | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/StarlingMaid-2x7B-base-GGUF/resolve/main/StarlingMaid-2x7B-base.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/StarlingMaid-2x7B-base-GGUF/resolve/main/StarlingMaid-2x7B-base.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/StarlingMaid-2x7B-base-GGUF/resolve/main/StarlingMaid-2x7B-base.Q5_K_M.gguf) | Q5_K_M | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/StarlingMaid-2x7B-base-GGUF/resolve/main/StarlingMaid-2x7B-base.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/StarlingMaid-2x7B-base-GGUF/resolve/main/StarlingMaid-2x7B-base.Q8_0.gguf) | Q8_0 | 13.9 | 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
## 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 -->
|
mradermacher/Miqu-MS-70B-i1-GGUF | mradermacher | 2024-05-06T05:19:24Z | 40 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Undi95/Miqu-MS-70B",
"base_model:quantized:Undi95/Miqu-MS-70B",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-04T01:01:01Z | ---
base_model: Undi95/Miqu-MS-70B
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/Undi95/Miqu-MS-70B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Miqu-MS-70B-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/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-IQ1_S.gguf) | i1-IQ1_S | 15.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-IQ1_M.gguf) | i1-IQ1_M | 16.4 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 18.7 | |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 20.8 | |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-IQ2_S.gguf) | i1-IQ2_S | 21.8 | |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-IQ2_M.gguf) | i1-IQ2_M | 23.7 | |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-Q2_K.gguf) | i1-Q2_K | 25.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 27.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 28.7 | |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-IQ3_S.gguf) | i1-IQ3_S | 30.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 30.3 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-IQ3_M.gguf) | i1-IQ3_M | 31.4 | |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 33.7 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 36.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-Q4_0.gguf) | i1-Q4_0 | 39.4 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 39.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 41.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 47.9 | |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 49.2 | |
| [PART 1](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF/resolve/main/Miqu-MS-70B.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 57.0 | practically like static Q6_K |
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
## 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 -->
|
mradermacher/Starling-LM-10.7B-beta-GGUF | mradermacher | 2024-05-06T05:19:11Z | 4 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:ddh0/Starling-LM-10.7B-beta",
"base_model:quantized:ddh0/Starling-LM-10.7B-beta",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-04T02:05:48Z | ---
base_model: ddh0/Starling-LM-10.7B-beta
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ddh0/Starling-LM-10.7B-beta
<!-- 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/Starling-LM-10.7B-beta-GGUF/resolve/main/Starling-LM-10.7B-beta.Q2_K.gguf) | Q2_K | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Starling-LM-10.7B-beta-GGUF/resolve/main/Starling-LM-10.7B-beta.IQ3_XS.gguf) | IQ3_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/Starling-LM-10.7B-beta-GGUF/resolve/main/Starling-LM-10.7B-beta.Q3_K_S.gguf) | Q3_K_S | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Starling-LM-10.7B-beta-GGUF/resolve/main/Starling-LM-10.7B-beta.IQ3_S.gguf) | IQ3_S | 4.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Starling-LM-10.7B-beta-GGUF/resolve/main/Starling-LM-10.7B-beta.IQ3_M.gguf) | IQ3_M | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Starling-LM-10.7B-beta-GGUF/resolve/main/Starling-LM-10.7B-beta.Q3_K_M.gguf) | Q3_K_M | 5.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Starling-LM-10.7B-beta-GGUF/resolve/main/Starling-LM-10.7B-beta.Q3_K_L.gguf) | Q3_K_L | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Starling-LM-10.7B-beta-GGUF/resolve/main/Starling-LM-10.7B-beta.IQ4_XS.gguf) | IQ4_XS | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/Starling-LM-10.7B-beta-GGUF/resolve/main/Starling-LM-10.7B-beta.Q4_K_S.gguf) | Q4_K_S | 6.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Starling-LM-10.7B-beta-GGUF/resolve/main/Starling-LM-10.7B-beta.Q4_K_M.gguf) | Q4_K_M | 6.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Starling-LM-10.7B-beta-GGUF/resolve/main/Starling-LM-10.7B-beta.Q5_K_S.gguf) | Q5_K_S | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/Starling-LM-10.7B-beta-GGUF/resolve/main/Starling-LM-10.7B-beta.Q5_K_M.gguf) | Q5_K_M | 7.9 | |
| [GGUF](https://huggingface.co/mradermacher/Starling-LM-10.7B-beta-GGUF/resolve/main/Starling-LM-10.7B-beta.Q6_K.gguf) | Q6_K | 9.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Starling-LM-10.7B-beta-GGUF/resolve/main/Starling-LM-10.7B-beta.Q8_0.gguf) | Q8_0 | 11.6 | 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
## 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 -->
|
mradermacher/AuroraRP-8x7B-GGUF | mradermacher | 2024-05-06T05:18:57Z | 26 | 1 | transformers | [
"transformers",
"gguf",
"roleplay",
"rp",
"mergekit",
"merge",
"en",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T04:00:24Z | ---
base_model: Fredithefish/AuroraRP-8x7B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- roleplay
- rp
- mergekit
- merge
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Fredithefish/AuroraRP-8x7B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/AuroraRP-8x7B-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/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q2_K.gguf) | Q2_K | 17.6 | |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.IQ3_XS.gguf) | IQ3_XS | 19.5 | |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.IQ3_S.gguf) | IQ3_S | 20.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q3_K_S.gguf) | Q3_K_S | 20.7 | |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.IQ3_M.gguf) | IQ3_M | 21.7 | |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q3_K_M.gguf) | Q3_K_M | 22.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q3_K_L.gguf) | Q3_K_L | 24.4 | |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.IQ4_XS.gguf) | IQ4_XS | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q4_K_S.gguf) | Q4_K_S | 27.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q4_K_M.gguf) | Q4_K_M | 28.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q5_K_S.gguf) | Q5_K_S | 32.5 | |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q5_K_M.gguf) | Q5_K_M | 33.5 | |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q6_K.gguf) | Q6_K | 38.6 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q8_0.gguf.part2of2) | Q8_0 | 49.8 | 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
## 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 -->
|
mradermacher/airoboros-l2-70b-2.2-i1-GGUF | mradermacher | 2024-05-06T05:18:52Z | 54 | 0 | transformers | [
"transformers",
"gguf",
"en",
"dataset:jondurbin/airoboros-2.2",
"base_model:jondurbin/airoboros-l2-70b-2.2",
"base_model:quantized:jondurbin/airoboros-l2-70b-2.2",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T04:00:39Z | ---
base_model: jondurbin/airoboros-l2-70b-2.2
datasets:
- jondurbin/airoboros-2.2
language:
- en
library_name: transformers
license: llama2
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/jondurbin/airoboros-l2-70b-2.2
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-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/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-IQ1_S.gguf) | i1-IQ1_S | 15.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-IQ1_M.gguf) | i1-IQ1_M | 16.4 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 18.7 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 20.8 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-IQ2_S.gguf) | i1-IQ2_S | 21.8 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-IQ2_M.gguf) | i1-IQ2_M | 23.7 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-Q2_K.gguf) | i1-Q2_K | 25.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 27.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 28.7 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-IQ3_S.gguf) | i1-IQ3_S | 30.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 30.3 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-IQ3_M.gguf) | i1-IQ3_M | 31.4 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 33.7 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 36.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-Q4_0.gguf) | i1-Q4_0 | 39.4 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 39.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 41.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 47.9 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 49.2 | |
| [PART 1](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/airoboros-l2-70b-2.2-i1-GGUF/resolve/main/airoboros-l2-70b-2.2.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 57.0 | practically like static Q6_K |
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
## 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 -->
|
mradermacher/Mavens-2-GGUF | mradermacher | 2024-05-06T05:18:49Z | 322 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"en",
"base_model:AiMavenAi/Mavens-2",
"base_model:quantized:AiMavenAi/Mavens-2",
"license:cc-by-nc-nd-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T05:06:34Z | ---
base_model: AiMavenAi/Mavens-2
language:
- en
library_name: transformers
license: cc-by-nc-nd-4.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/AiMavenAi/Mavens-2
<!-- 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/Mavens-2-GGUF/resolve/main/Mavens-2.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mavens-2-GGUF/resolve/main/Mavens-2.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mavens-2-GGUF/resolve/main/Mavens-2.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mavens-2-GGUF/resolve/main/Mavens-2.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mavens-2-GGUF/resolve/main/Mavens-2.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mavens-2-GGUF/resolve/main/Mavens-2.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mavens-2-GGUF/resolve/main/Mavens-2.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mavens-2-GGUF/resolve/main/Mavens-2.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mavens-2-GGUF/resolve/main/Mavens-2.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mavens-2-GGUF/resolve/main/Mavens-2.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mavens-2-GGUF/resolve/main/Mavens-2.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mavens-2-GGUF/resolve/main/Mavens-2.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mavens-2-GGUF/resolve/main/Mavens-2.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mavens-2-GGUF/resolve/main/Mavens-2.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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 -->
|
mradermacher/HyouKan-3x7B-V2-32k-GGUF | mradermacher | 2024-05-06T05:18:41Z | 55 | 0 | transformers | [
"transformers",
"gguf",
"moe",
"merge",
"Roleplay",
"en",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T06:17:02Z | ---
base_model: Alsebay/HyouKan-3x7B-V2-32k
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- moe
- merge
- Roleplay
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Alsebay/HyouKan-3x7B-V2-32k
<!-- 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/HyouKan-3x7B-V2-32k-GGUF/resolve/main/HyouKan-3x7B-V2-32k.Q2_K.gguf) | Q2_K | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-V2-32k-GGUF/resolve/main/HyouKan-3x7B-V2-32k.IQ3_XS.gguf) | IQ3_XS | 7.8 | |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-V2-32k-GGUF/resolve/main/HyouKan-3x7B-V2-32k.Q3_K_S.gguf) | Q3_K_S | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-V2-32k-GGUF/resolve/main/HyouKan-3x7B-V2-32k.IQ3_S.gguf) | IQ3_S | 8.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-V2-32k-GGUF/resolve/main/HyouKan-3x7B-V2-32k.IQ3_M.gguf) | IQ3_M | 8.4 | |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-V2-32k-GGUF/resolve/main/HyouKan-3x7B-V2-32k.Q3_K_M.gguf) | Q3_K_M | 9.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-V2-32k-GGUF/resolve/main/HyouKan-3x7B-V2-32k.Q3_K_L.gguf) | Q3_K_L | 9.9 | |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-V2-32k-GGUF/resolve/main/HyouKan-3x7B-V2-32k.IQ4_XS.gguf) | IQ4_XS | 10.3 | |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-V2-32k-GGUF/resolve/main/HyouKan-3x7B-V2-32k.Q4_K_S.gguf) | Q4_K_S | 10.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-V2-32k-GGUF/resolve/main/HyouKan-3x7B-V2-32k.Q4_K_M.gguf) | Q4_K_M | 11.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-V2-32k-GGUF/resolve/main/HyouKan-3x7B-V2-32k.Q5_K_S.gguf) | Q5_K_S | 13.0 | |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-V2-32k-GGUF/resolve/main/HyouKan-3x7B-V2-32k.Q5_K_M.gguf) | Q5_K_M | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-V2-32k-GGUF/resolve/main/HyouKan-3x7B-V2-32k.Q6_K.gguf) | Q6_K | 15.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-V2-32k-GGUF/resolve/main/HyouKan-3x7B-V2-32k.Q8_0.gguf) | Q8_0 | 19.9 | 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
## 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 -->
|
mradermacher/MistarlingMaid-2x7B-base-GGUF | mradermacher | 2024-05-06T05:18:27Z | 74 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:dawn17/MistarlingMaid-2x7B-base",
"base_model:quantized:dawn17/MistarlingMaid-2x7B-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-04T08:20:09Z | ---
base_model: dawn17/MistarlingMaid-2x7B-base
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/dawn17/MistarlingMaid-2x7B-base
<!-- 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/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.IQ3_XS.gguf) | IQ3_XS | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.IQ3_S.gguf) | IQ3_S | 5.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.IQ3_M.gguf) | IQ3_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q3_K_M.gguf) | Q3_K_M | 6.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.IQ4_XS.gguf) | IQ4_XS | 7.2 | |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q4_K_S.gguf) | Q4_K_S | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q5_K_M.gguf) | Q5_K_M | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q8_0.gguf) | Q8_0 | 13.9 | 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
## 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 -->
|
mradermacher/TripleMerge2-7B-Ties-GGUF | mradermacher | 2024-05-06T05:18:25Z | 20 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"allknowingroger/LimyQstar-7B-slerp",
"allknowingroger/JaskierMistral-7B-slerp",
"allknowingroger/LimmyAutomerge-7B-slerp",
"en",
"base_model:allknowingroger/TripleMerge2-7B-Ties",
"base_model:quantized:allknowingroger/TripleMerge2-7B-Ties",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T08:25:34Z | ---
base_model: allknowingroger/TripleMerge2-7B-Ties
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- allknowingroger/LimyQstar-7B-slerp
- allknowingroger/JaskierMistral-7B-slerp
- allknowingroger/LimmyAutomerge-7B-slerp
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/allknowingroger/TripleMerge2-7B-Ties
<!-- 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/TripleMerge2-7B-Ties-GGUF/resolve/main/TripleMerge2-7B-Ties.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/TripleMerge2-7B-Ties-GGUF/resolve/main/TripleMerge2-7B-Ties.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/TripleMerge2-7B-Ties-GGUF/resolve/main/TripleMerge2-7B-Ties.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/TripleMerge2-7B-Ties-GGUF/resolve/main/TripleMerge2-7B-Ties.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/TripleMerge2-7B-Ties-GGUF/resolve/main/TripleMerge2-7B-Ties.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/TripleMerge2-7B-Ties-GGUF/resolve/main/TripleMerge2-7B-Ties.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/TripleMerge2-7B-Ties-GGUF/resolve/main/TripleMerge2-7B-Ties.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/TripleMerge2-7B-Ties-GGUF/resolve/main/TripleMerge2-7B-Ties.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/TripleMerge2-7B-Ties-GGUF/resolve/main/TripleMerge2-7B-Ties.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TripleMerge2-7B-Ties-GGUF/resolve/main/TripleMerge2-7B-Ties.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TripleMerge2-7B-Ties-GGUF/resolve/main/TripleMerge2-7B-Ties.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/TripleMerge2-7B-Ties-GGUF/resolve/main/TripleMerge2-7B-Ties.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/TripleMerge2-7B-Ties-GGUF/resolve/main/TripleMerge2-7B-Ties.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/TripleMerge2-7B-Ties-GGUF/resolve/main/TripleMerge2-7B-Ties.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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 -->
|
mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF | mradermacher | 2024-05-06T05:18:21Z | 40 | 1 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Undi95/MythoMax-L2-Kimiko-v2-13b",
"base_model:quantized:Undi95/MythoMax-L2-Kimiko-v2-13b",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T09:08:24Z | ---
base_model: Undi95/MythoMax-L2-Kimiko-v2-13b
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Undi95/MythoMax-L2-Kimiko-v2-13b
<!-- 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/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.Q2_K.gguf) | Q2_K | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.IQ3_XS.gguf) | IQ3_XS | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.IQ3_S.gguf) | IQ3_S | 6.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.Q3_K_S.gguf) | Q3_K_S | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.IQ3_M.gguf) | IQ3_M | 6.3 | |
| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.Q3_K_M.gguf) | Q3_K_M | 6.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.Q3_K_L.gguf) | Q3_K_L | 7.2 | |
| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.IQ4_XS.gguf) | IQ4_XS | 7.3 | |
| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.Q4_K_S.gguf) | Q4_K_S | 7.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.Q4_K_M.gguf) | Q4_K_M | 8.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.Q5_K_S.gguf) | Q5_K_S | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.Q5_K_M.gguf) | Q5_K_M | 9.5 | |
| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.Q6_K.gguf) | Q6_K | 11.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.Q8_0.gguf) | Q8_0 | 14.1 | 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
## 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 -->
|
mradermacher/meerkat-7b-v1.0-GGUF | mradermacher | 2024-05-06T05:18:17Z | 70 | 0 | transformers | [
"transformers",
"gguf",
"medical",
"small LM",
"instruction-tuned",
"usmle",
"chain-of-thought",
"synthetic data",
"en",
"base_model:dmis-lab/meerkat-7b-v1.0",
"base_model:quantized:dmis-lab/meerkat-7b-v1.0",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-04T09:27:33Z | ---
base_model: dmis-lab/meerkat-7b-v1.0
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- medical
- small LM
- instruction-tuned
- usmle
- chain-of-thought
- synthetic data
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/dmis-lab/meerkat-7b-v1.0
<!-- 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/meerkat-7b-v1.0-GGUF/resolve/main/meerkat-7b-v1.0.Q2_K.gguf) | Q2_K | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/meerkat-7b-v1.0-GGUF/resolve/main/meerkat-7b-v1.0.IQ3_XS.gguf) | IQ3_XS | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/meerkat-7b-v1.0-GGUF/resolve/main/meerkat-7b-v1.0.Q3_K_S.gguf) | Q3_K_S | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/meerkat-7b-v1.0-GGUF/resolve/main/meerkat-7b-v1.0.IQ3_S.gguf) | IQ3_S | 3.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/meerkat-7b-v1.0-GGUF/resolve/main/meerkat-7b-v1.0.IQ3_M.gguf) | IQ3_M | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/meerkat-7b-v1.0-GGUF/resolve/main/meerkat-7b-v1.0.Q3_K_M.gguf) | Q3_K_M | 4.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/meerkat-7b-v1.0-GGUF/resolve/main/meerkat-7b-v1.0.Q3_K_L.gguf) | Q3_K_L | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/meerkat-7b-v1.0-GGUF/resolve/main/meerkat-7b-v1.0.IQ4_XS.gguf) | IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/meerkat-7b-v1.0-GGUF/resolve/main/meerkat-7b-v1.0.Q4_K_S.gguf) | Q4_K_S | 4.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/meerkat-7b-v1.0-GGUF/resolve/main/meerkat-7b-v1.0.Q4_K_M.gguf) | Q4_K_M | 4.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/meerkat-7b-v1.0-GGUF/resolve/main/meerkat-7b-v1.0.Q5_K_S.gguf) | Q5_K_S | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/meerkat-7b-v1.0-GGUF/resolve/main/meerkat-7b-v1.0.Q5_K_M.gguf) | Q5_K_M | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/meerkat-7b-v1.0-GGUF/resolve/main/meerkat-7b-v1.0.Q6_K.gguf) | Q6_K | 6.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/meerkat-7b-v1.0-GGUF/resolve/main/meerkat-7b-v1.0.Q8_0.gguf) | Q8_0 | 8.2 | 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
## 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 -->
|
mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF | mradermacher | 2024-05-06T05:18:04Z | 37 | 2 | transformers | [
"transformers",
"gguf",
"mergekit",
"megamerge",
"code",
"Cyber-Series",
"en",
"dataset:Open-Orca/OpenOrca",
"dataset:cognitivecomputations/dolphin",
"dataset:WhiteRabbitNeo/WRN-Chapter-2",
"dataset:WhiteRabbitNeo/WRN-Chapter-1",
"dataset:gate369/Alpaca-Star",
"dataset:gate369/alpaca-star-ascii",
"base_model:LeroyDyer/Mixtral_AI_Cyber_Matrix_2_0",
"base_model:quantized:LeroyDyer/Mixtral_AI_Cyber_Matrix_2_0",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-04T10:11:11Z | ---
base_model: LeroyDyer/Mixtral_AI_Cyber_Matrix_2_0
datasets:
- Open-Orca/OpenOrca
- cognitivecomputations/dolphin
- WhiteRabbitNeo/WRN-Chapter-2
- WhiteRabbitNeo/WRN-Chapter-1
- gate369/Alpaca-Star
- gate369/alpaca-star-ascii
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
tags:
- mergekit
- megamerge
- code
- Cyber-Series
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/LeroyDyer/Mixtral_AI_Cyber_Matrix_2_0
<!-- 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/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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 -->
|
mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF | mradermacher | 2024-05-06T05:17:59Z | 1 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:ddh0/Mistral-10.7B-Instruct-v0.2",
"base_model:quantized:ddh0/Mistral-10.7B-Instruct-v0.2",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-04T12:08:09Z | ---
base_model: ddh0/Mistral-10.7B-Instruct-v0.2
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ddh0/Mistral-10.7B-Instruct-v0.2
<!-- 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/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q2_K.gguf) | Q2_K | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.IQ3_XS.gguf) | IQ3_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q3_K_S.gguf) | Q3_K_S | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.IQ3_S.gguf) | IQ3_S | 4.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.IQ3_M.gguf) | IQ3_M | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q3_K_M.gguf) | Q3_K_M | 5.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q3_K_L.gguf) | Q3_K_L | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.IQ4_XS.gguf) | IQ4_XS | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q4_K_S.gguf) | Q4_K_S | 6.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q4_K_M.gguf) | Q4_K_M | 6.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q5_K_S.gguf) | Q5_K_S | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q5_K_M.gguf) | Q5_K_M | 7.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q6_K.gguf) | Q6_K | 9.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q8_0.gguf) | Q8_0 | 11.6 | 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
## 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 -->
|
mradermacher/megatron_2.1_MoE_2x7B-GGUF | mradermacher | 2024-05-06T05:17:51Z | 1 | 0 | transformers | [
"transformers",
"gguf",
"moe",
"merge",
"en",
"base_model:Eurdem/megatron_2.1_MoE_2x7B",
"base_model:quantized:Eurdem/megatron_2.1_MoE_2x7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T12:13:09Z | ---
base_model: Eurdem/megatron_2.1_MoE_2x7B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- moe
- merge
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Eurdem/megatron_2.1_MoE_2x7B
<!-- 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/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.IQ3_XS.gguf) | IQ3_XS | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.IQ3_S.gguf) | IQ3_S | 5.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.IQ3_M.gguf) | IQ3_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.Q3_K_M.gguf) | Q3_K_M | 6.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.IQ4_XS.gguf) | IQ4_XS | 7.2 | |
| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.Q4_K_S.gguf) | Q4_K_S | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.Q5_K_M.gguf) | Q5_K_M | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.Q8_0.gguf) | Q8_0 | 13.9 | 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
## 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 -->
|
mradermacher/CatNyanster-34b-GGUF | mradermacher | 2024-05-06T05:17:44Z | 59 | 1 | transformers | [
"transformers",
"gguf",
"merge",
"roleplay",
"chat",
"en",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T13:04:35Z | ---
base_model: arlineka/CatNyanster-34b
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- merge
- roleplay
- chat
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/arlineka/CatNyanster-34b
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/CatNyanster-34b-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/CatNyanster-34b-GGUF/resolve/main/CatNyanster-34b.Q2_K.gguf) | Q2_K | 13.5 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-GGUF/resolve/main/CatNyanster-34b.IQ3_XS.gguf) | IQ3_XS | 14.9 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-GGUF/resolve/main/CatNyanster-34b.Q3_K_S.gguf) | Q3_K_S | 15.6 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-GGUF/resolve/main/CatNyanster-34b.IQ3_S.gguf) | IQ3_S | 15.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-GGUF/resolve/main/CatNyanster-34b.IQ3_M.gguf) | IQ3_M | 16.2 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-GGUF/resolve/main/CatNyanster-34b.Q3_K_M.gguf) | Q3_K_M | 17.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-GGUF/resolve/main/CatNyanster-34b.Q3_K_L.gguf) | Q3_K_L | 18.8 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-GGUF/resolve/main/CatNyanster-34b.IQ4_XS.gguf) | IQ4_XS | 19.3 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-GGUF/resolve/main/CatNyanster-34b.Q4_K_S.gguf) | Q4_K_S | 20.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-GGUF/resolve/main/CatNyanster-34b.Q4_K_M.gguf) | Q4_K_M | 21.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-GGUF/resolve/main/CatNyanster-34b.Q5_K_S.gguf) | Q5_K_S | 24.3 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-GGUF/resolve/main/CatNyanster-34b.Q5_K_M.gguf) | Q5_K_M | 25.0 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-GGUF/resolve/main/CatNyanster-34b.Q6_K.gguf) | Q6_K | 28.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-GGUF/resolve/main/CatNyanster-34b.Q8_0.gguf) | Q8_0 | 37.1 | 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
## 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 -->
|
mradermacher/CatNyanster-34b-i1-GGUF | mradermacher | 2024-05-06T05:17:35Z | 4 | 0 | transformers | [
"transformers",
"gguf",
"en",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T15:33:46Z | ---
base_model: arlineka/CatNyanster-34b
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/arlineka/CatNyanster-34b
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/CatNyanster-34b-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/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ1_S.gguf) | i1-IQ1_S | 8.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ1_M.gguf) | i1-IQ1_M | 8.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 10.0 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 11.0 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ2_S.gguf) | i1-IQ2_S | 11.6 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ2_M.gguf) | i1-IQ2_M | 12.5 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-Q2_K.gguf) | i1-Q2_K | 13.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 14.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 14.9 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 15.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ3_S.gguf) | i1-IQ3_S | 15.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ3_M.gguf) | i1-IQ3_M | 16.2 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 17.3 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 18.8 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 19.1 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-Q4_0.gguf) | i1-Q4_0 | 20.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 20.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 21.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 24.3 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 25.0 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-Q6_K.gguf) | i1-Q6_K | 28.9 | practically like static Q6_K |
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
## 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 -->
|
mradermacher/XuanYuan-70B-Chat-GGUF | mradermacher | 2024-05-06T05:17:20Z | 36 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Duxiaoman-DI/XuanYuan-70B-Chat",
"base_model:quantized:Duxiaoman-DI/XuanYuan-70B-Chat",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T16:20:39Z | ---
base_model: Duxiaoman-DI/XuanYuan-70B-Chat
language:
- en
library_name: transformers
license: llama2
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Duxiaoman-DI/XuanYuan-70B-Chat
<!-- 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/XuanYuan-70B-Chat-GGUF/resolve/main/XuanYuan-70B-Chat.Q2_K.gguf) | Q2_K | 26.0 | |
| [GGUF](https://huggingface.co/mradermacher/XuanYuan-70B-Chat-GGUF/resolve/main/XuanYuan-70B-Chat.IQ3_XS.gguf) | IQ3_XS | 28.9 | |
| [GGUF](https://huggingface.co/mradermacher/XuanYuan-70B-Chat-GGUF/resolve/main/XuanYuan-70B-Chat.IQ3_S.gguf) | IQ3_S | 30.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/XuanYuan-70B-Chat-GGUF/resolve/main/XuanYuan-70B-Chat.Q3_K_S.gguf) | Q3_K_S | 30.5 | |
| [GGUF](https://huggingface.co/mradermacher/XuanYuan-70B-Chat-GGUF/resolve/main/XuanYuan-70B-Chat.IQ3_M.gguf) | IQ3_M | 31.5 | |
| [GGUF](https://huggingface.co/mradermacher/XuanYuan-70B-Chat-GGUF/resolve/main/XuanYuan-70B-Chat.Q3_K_M.gguf) | Q3_K_M | 33.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/XuanYuan-70B-Chat-GGUF/resolve/main/XuanYuan-70B-Chat.Q3_K_L.gguf) | Q3_K_L | 36.7 | |
| [GGUF](https://huggingface.co/mradermacher/XuanYuan-70B-Chat-GGUF/resolve/main/XuanYuan-70B-Chat.IQ4_XS.gguf) | IQ4_XS | 37.8 | |
| [GGUF](https://huggingface.co/mradermacher/XuanYuan-70B-Chat-GGUF/resolve/main/XuanYuan-70B-Chat.Q4_K_S.gguf) | Q4_K_S | 39.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/XuanYuan-70B-Chat-GGUF/resolve/main/XuanYuan-70B-Chat.Q4_K_M.gguf) | Q4_K_M | 42.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/XuanYuan-70B-Chat-GGUF/resolve/main/XuanYuan-70B-Chat.Q5_K_S.gguf) | Q5_K_S | 48.0 | |
| [GGUF](https://huggingface.co/mradermacher/XuanYuan-70B-Chat-GGUF/resolve/main/XuanYuan-70B-Chat.Q5_K_M.gguf) | Q5_K_M | 49.3 | |
| [PART 1](https://huggingface.co/mradermacher/XuanYuan-70B-Chat-GGUF/resolve/main/XuanYuan-70B-Chat.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/XuanYuan-70B-Chat-GGUF/resolve/main/XuanYuan-70B-Chat.Q6_K.gguf.part2of2) | Q6_K | 57.2 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/XuanYuan-70B-Chat-GGUF/resolve/main/XuanYuan-70B-Chat.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/XuanYuan-70B-Chat-GGUF/resolve/main/XuanYuan-70B-Chat.Q8_0.gguf.part2of2) | Q8_0 | 73.8 | 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
## 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 -->
|
mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF | mradermacher | 2024-05-06T05:17:03Z | 14 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"Yi",
"en",
"base_model:brucethemoose/Yi-34B-200K-DARE-megamerge-v8",
"base_model:quantized:brucethemoose/Yi-34B-200K-DARE-megamerge-v8",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T21:19:38Z | ---
base_model: brucethemoose/Yi-34B-200K-DARE-megamerge-v8
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
license_name: yi-license
quantized_by: mradermacher
tags:
- mergekit
- merge
- Yi
---
## About
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-megamerge-v8
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-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/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-IQ1_S.gguf) | i1-IQ1_S | 7.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-IQ1_M.gguf) | i1-IQ1_M | 8.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-IQ2_S.gguf) | i1-IQ2_S | 11.0 | |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-IQ2_M.gguf) | i1-IQ2_M | 11.9 | |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-Q2_K.gguf) | i1-Q2_K | 12.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 13.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-IQ3_XS.gguf) | i1-IQ3_XS | 14.3 | |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-Q3_K_S.gguf) | i1-Q3_K_S | 15.1 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-IQ3_S.gguf) | i1-IQ3_S | 15.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-IQ3_M.gguf) | i1-IQ3_M | 15.7 | |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-Q3_K_L.gguf) | i1-Q3_K_L | 18.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-IQ4_XS.gguf) | i1-IQ4_XS | 18.6 | |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-Q4_0.gguf) | i1-Q4_0 | 19.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-Q4_K_S.gguf) | i1-Q4_K_S | 19.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-Q5_K_S.gguf) | i1-Q5_K_S | 23.8 | |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-Q5_K_M.gguf) | i1-Q5_K_M | 24.4 | |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-DARE-megamerge-v8-i1-GGUF/resolve/main/Yi-34B-200K-DARE-megamerge-v8.i1-Q6_K.gguf) | i1-Q6_K | 28.3 | practically like static Q6_K |
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
## 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 -->
|
mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF | mradermacher | 2024-05-06T05:16:57Z | 2 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:TroyDoesAI/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic",
"base_model:quantized:TroyDoesAI/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T22:43:02Z | ---
base_model: TroyDoesAI/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic
language:
- en
library_name: transformers
license: cc-by-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/TroyDoesAI/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic
<!-- 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/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.Q2_K.gguf) | Q2_K | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.IQ3_XS.gguf) | IQ3_XS | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.Q3_K_S.gguf) | Q3_K_S | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.IQ3_S.gguf) | IQ3_S | 4.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.IQ3_M.gguf) | IQ3_M | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.Q3_K_M.gguf) | Q3_K_M | 4.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.Q3_K_L.gguf) | Q3_K_L | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.IQ4_XS.gguf) | IQ4_XS | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.Q4_K_S.gguf) | Q4_K_S | 5.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.Q4_K_M.gguf) | Q4_K_M | 5.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.Q5_K_S.gguf) | Q5_K_S | 6.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.Q5_K_M.gguf) | Q5_K_M | 6.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.Q6_K.gguf) | Q6_K | 7.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.Q8_0.gguf) | Q8_0 | 9.5 | 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
## 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 -->
|
mradermacher/Young-Children-Storyteller-Mistral-7B-GGUF | mradermacher | 2024-05-06T05:16:55Z | 126 | 1 | transformers | [
"transformers",
"gguf",
"story",
"young children",
"educational",
"knowledge",
"en",
"dataset:ajibawa-2023/Children-Stories-Collection",
"base_model:ajibawa-2023/Young-Children-Storyteller-Mistral-7B",
"base_model:quantized:ajibawa-2023/Young-Children-Storyteller-Mistral-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-04T23:59:06Z | ---
base_model: ajibawa-2023/Young-Children-Storyteller-Mistral-7B
datasets:
- ajibawa-2023/Children-Stories-Collection
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- story
- young children
- educational
- knowledge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ajibawa-2023/Young-Children-Storyteller-Mistral-7B
<!-- 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/Young-Children-Storyteller-Mistral-7B-GGUF/resolve/main/Young-Children-Storyteller-Mistral-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Young-Children-Storyteller-Mistral-7B-GGUF/resolve/main/Young-Children-Storyteller-Mistral-7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Young-Children-Storyteller-Mistral-7B-GGUF/resolve/main/Young-Children-Storyteller-Mistral-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Young-Children-Storyteller-Mistral-7B-GGUF/resolve/main/Young-Children-Storyteller-Mistral-7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Young-Children-Storyteller-Mistral-7B-GGUF/resolve/main/Young-Children-Storyteller-Mistral-7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Young-Children-Storyteller-Mistral-7B-GGUF/resolve/main/Young-Children-Storyteller-Mistral-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Young-Children-Storyteller-Mistral-7B-GGUF/resolve/main/Young-Children-Storyteller-Mistral-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Young-Children-Storyteller-Mistral-7B-GGUF/resolve/main/Young-Children-Storyteller-Mistral-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Young-Children-Storyteller-Mistral-7B-GGUF/resolve/main/Young-Children-Storyteller-Mistral-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Young-Children-Storyteller-Mistral-7B-GGUF/resolve/main/Young-Children-Storyteller-Mistral-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Young-Children-Storyteller-Mistral-7B-GGUF/resolve/main/Young-Children-Storyteller-Mistral-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Young-Children-Storyteller-Mistral-7B-GGUF/resolve/main/Young-Children-Storyteller-Mistral-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Young-Children-Storyteller-Mistral-7B-GGUF/resolve/main/Young-Children-Storyteller-Mistral-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Young-Children-Storyteller-Mistral-7B-GGUF/resolve/main/Young-Children-Storyteller-Mistral-7B.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/CatNyanster-7b-GGUF | mradermacher | 2024-05-06T05:16:49Z | 17 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"en",
"base_model:arlineka/CatNyanster-7b",
"base_model:quantized:arlineka/CatNyanster-7b",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T00:44:13Z | ---
base_model: arlineka/CatNyanster-7b
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/arlineka/CatNyanster-7b
<!-- 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/CatNyanster-7b-GGUF/resolve/main/CatNyanster-7b.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-7b-GGUF/resolve/main/CatNyanster-7b.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-7b-GGUF/resolve/main/CatNyanster-7b.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-7b-GGUF/resolve/main/CatNyanster-7b.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-7b-GGUF/resolve/main/CatNyanster-7b.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-7b-GGUF/resolve/main/CatNyanster-7b.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-7b-GGUF/resolve/main/CatNyanster-7b.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-7b-GGUF/resolve/main/CatNyanster-7b.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-7b-GGUF/resolve/main/CatNyanster-7b.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-7b-GGUF/resolve/main/CatNyanster-7b.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-7b-GGUF/resolve/main/CatNyanster-7b.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-7b-GGUF/resolve/main/CatNyanster-7b.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-7b-GGUF/resolve/main/CatNyanster-7b.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/CatNyanster-7b-GGUF/resolve/main/CatNyanster-7b.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/NeuralNinja-2x-7B-GGUF | mradermacher | 2024-05-06T05:16:45Z | 2 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Muhammad2003/NeuralNinja-2x-7B",
"base_model:quantized:Muhammad2003/NeuralNinja-2x-7B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-05T00:55:22Z | ---
base_model: Muhammad2003/NeuralNinja-2x-7B
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Muhammad2003/NeuralNinja-2x-7B
<!-- 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/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.IQ3_XS.gguf) | IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.Q3_K_S.gguf) | Q3_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.IQ3_S.gguf) | IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.IQ3_M.gguf) | IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.Q3_K_M.gguf) | Q3_K_M | 6.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.Q3_K_L.gguf) | Q3_K_L | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.Q4_K_S.gguf) | Q4_K_S | 7.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.Q4_K_M.gguf) | Q4_K_M | 7.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.Q5_K_S.gguf) | Q5_K_S | 9.0 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.Q5_K_M.gguf) | Q5_K_M | 9.2 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.Q6_K.gguf) | Q6_K | 10.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.Q8_0.gguf) | Q8_0 | 13.8 | 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
## 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 -->
|
mradermacher/Cypher-7B-GGUF | mradermacher | 2024-05-06T05:16:40Z | 20 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"mistral",
"nous",
"westlake",
"samantha",
"en",
"base_model:aloobun/Cypher-7B",
"base_model:quantized:aloobun/Cypher-7B",
"license:cc",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-05T01:45:10Z | ---
base_model: aloobun/Cypher-7B
language:
- en
library_name: transformers
license: cc
quantized_by: mradermacher
tags:
- mergekit
- merge
- mistral
- nous
- westlake
- samantha
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/aloobun/Cypher-7B
<!-- 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/Cypher-7B-GGUF/resolve/main/Cypher-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Cypher-7B-GGUF/resolve/main/Cypher-7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Cypher-7B-GGUF/resolve/main/Cypher-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Cypher-7B-GGUF/resolve/main/Cypher-7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Cypher-7B-GGUF/resolve/main/Cypher-7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Cypher-7B-GGUF/resolve/main/Cypher-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Cypher-7B-GGUF/resolve/main/Cypher-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Cypher-7B-GGUF/resolve/main/Cypher-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Cypher-7B-GGUF/resolve/main/Cypher-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Cypher-7B-GGUF/resolve/main/Cypher-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Cypher-7B-GGUF/resolve/main/Cypher-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Cypher-7B-GGUF/resolve/main/Cypher-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Cypher-7B-GGUF/resolve/main/Cypher-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Cypher-7B-GGUF/resolve/main/Cypher-7B.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/WizardLM-30B-V1.0-GGUF | mradermacher | 2024-05-06T05:16:35Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:WizardLM/WizardLM-30B-V1.0",
"base_model:quantized:WizardLM/WizardLM-30B-V1.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T01:57:20Z | ---
base_model: WizardLM/WizardLM-30B-V1.0
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/WizardLM/WizardLM-30B-V1.0
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/WizardLM-30B-V1.0-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/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.Q2_K.gguf) | Q2_K | 12.1 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.IQ3_XS.gguf) | IQ3_XS | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.IQ3_S.gguf) | IQ3_S | 14.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.Q3_K_S.gguf) | Q3_K_S | 14.2 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.IQ3_M.gguf) | IQ3_M | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.Q3_K_M.gguf) | Q3_K_M | 15.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.Q3_K_L.gguf) | Q3_K_L | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.IQ4_XS.gguf) | IQ4_XS | 17.6 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.Q4_K_S.gguf) | Q4_K_S | 18.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.Q4_K_M.gguf) | Q4_K_M | 19.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.Q5_K_S.gguf) | Q5_K_S | 22.5 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.Q5_K_M.gguf) | Q5_K_M | 23.1 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.Q6_K.gguf) | Q6_K | 26.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.Q8_0.gguf) | Q8_0 | 34.7 | 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
## 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 -->
|
mradermacher/dragonwar-7b-orpo-GGUF | mradermacher | 2024-05-06T05:16:32Z | 47 | 0 | transformers | [
"transformers",
"gguf",
"unsloth",
"book",
"en",
"dataset:vicgalle/OpenHermesPreferences-roleplay",
"base_model:maldv/dragonwar-7b-orpo",
"base_model:quantized:maldv/dragonwar-7b-orpo",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-05T02:07:39Z | ---
base_model: maldv/dragonwar-7b-orpo
datasets:
- vicgalle/OpenHermesPreferences-roleplay
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- unsloth
- book
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/maldv/dragonwar-7b-orpo
<!-- 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/dragonwar-7b-orpo-GGUF/resolve/main/dragonwar-7b-orpo.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-orpo-GGUF/resolve/main/dragonwar-7b-orpo.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-orpo-GGUF/resolve/main/dragonwar-7b-orpo.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-orpo-GGUF/resolve/main/dragonwar-7b-orpo.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-orpo-GGUF/resolve/main/dragonwar-7b-orpo.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-orpo-GGUF/resolve/main/dragonwar-7b-orpo.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-orpo-GGUF/resolve/main/dragonwar-7b-orpo.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-orpo-GGUF/resolve/main/dragonwar-7b-orpo.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-orpo-GGUF/resolve/main/dragonwar-7b-orpo.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-orpo-GGUF/resolve/main/dragonwar-7b-orpo.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-orpo-GGUF/resolve/main/dragonwar-7b-orpo.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-orpo-GGUF/resolve/main/dragonwar-7b-orpo.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-orpo-GGUF/resolve/main/dragonwar-7b-orpo.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-orpo-GGUF/resolve/main/dragonwar-7b-orpo.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/Irene-RP-v5-7B-GGUF | mradermacher | 2024-05-06T05:16:24Z | 1 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"mistral",
"roleplay",
"en",
"base_model:Virt-io/Irene-RP-v5-7B",
"base_model:quantized:Virt-io/Irene-RP-v5-7B",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T02:29:13Z | ---
base_model: Virt-io/Irene-RP-v5-7B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
- mistral
- roleplay
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Virt-io/Irene-RP-v5-7B
<!-- 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/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/MisCalmity-v0.1-model_stock-GGUF | mradermacher | 2024-05-06T05:16:19Z | 1 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"en",
"base_model:thag8/MisCalmity-v0.1-model_stock",
"base_model:quantized:thag8/MisCalmity-v0.1-model_stock",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T03:25:20Z | ---
base_model: thag8/MisCalmity-v0.1-model_stock
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/thag8/MisCalmity-v0.1-model_stock
<!-- 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/MisCalmity-v0.1-model_stock-GGUF/resolve/main/MisCalmity-v0.1-model_stock.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/MisCalmity-v0.1-model_stock-GGUF/resolve/main/MisCalmity-v0.1-model_stock.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/MisCalmity-v0.1-model_stock-GGUF/resolve/main/MisCalmity-v0.1-model_stock.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/MisCalmity-v0.1-model_stock-GGUF/resolve/main/MisCalmity-v0.1-model_stock.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/MisCalmity-v0.1-model_stock-GGUF/resolve/main/MisCalmity-v0.1-model_stock.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/MisCalmity-v0.1-model_stock-GGUF/resolve/main/MisCalmity-v0.1-model_stock.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MisCalmity-v0.1-model_stock-GGUF/resolve/main/MisCalmity-v0.1-model_stock.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/MisCalmity-v0.1-model_stock-GGUF/resolve/main/MisCalmity-v0.1-model_stock.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/MisCalmity-v0.1-model_stock-GGUF/resolve/main/MisCalmity-v0.1-model_stock.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MisCalmity-v0.1-model_stock-GGUF/resolve/main/MisCalmity-v0.1-model_stock.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MisCalmity-v0.1-model_stock-GGUF/resolve/main/MisCalmity-v0.1-model_stock.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/MisCalmity-v0.1-model_stock-GGUF/resolve/main/MisCalmity-v0.1-model_stock.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/MisCalmity-v0.1-model_stock-GGUF/resolve/main/MisCalmity-v0.1-model_stock.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/MisCalmity-v0.1-model_stock-GGUF/resolve/main/MisCalmity-v0.1-model_stock.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/Maxine-34B-stock-GGUF | mradermacher | 2024-05-06T05:15:53Z | 17 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"ConvexAI/Luminex-34B-v0.2",
"fblgit/UNA-34BeagleSimpleMath-32K-v1",
"chemistry",
"biology",
"math",
"en",
"base_model:louisbrulenaudet/Maxine-34B-stock",
"base_model:quantized:louisbrulenaudet/Maxine-34B-stock",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-05T06:16:31Z | ---
base_model: louisbrulenaudet/Maxine-34B-stock
language:
- en
library_name: transformers
license: apache-2.0
no_imatrix: 'GGML_ASSERT: llama.cpp/ggml-quants.c:11239: grid_index >= 0'
quantized_by: mradermacher
tags:
- merge
- mergekit
- ConvexAI/Luminex-34B-v0.2
- fblgit/UNA-34BeagleSimpleMath-32K-v1
- chemistry
- biology
- math
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/louisbrulenaudet/Maxine-34B-stock
<!-- 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/Maxine-34B-stock-GGUF/resolve/main/Maxine-34B-stock.Q2_K.gguf) | Q2_K | 12.9 | |
| [GGUF](https://huggingface.co/mradermacher/Maxine-34B-stock-GGUF/resolve/main/Maxine-34B-stock.IQ3_XS.gguf) | IQ3_XS | 14.3 | |
| [GGUF](https://huggingface.co/mradermacher/Maxine-34B-stock-GGUF/resolve/main/Maxine-34B-stock.Q3_K_S.gguf) | Q3_K_S | 15.1 | |
| [GGUF](https://huggingface.co/mradermacher/Maxine-34B-stock-GGUF/resolve/main/Maxine-34B-stock.IQ3_S.gguf) | IQ3_S | 15.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Maxine-34B-stock-GGUF/resolve/main/Maxine-34B-stock.IQ3_M.gguf) | IQ3_M | 15.7 | |
| [GGUF](https://huggingface.co/mradermacher/Maxine-34B-stock-GGUF/resolve/main/Maxine-34B-stock.Q3_K_M.gguf) | Q3_K_M | 16.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Maxine-34B-stock-GGUF/resolve/main/Maxine-34B-stock.Q3_K_L.gguf) | Q3_K_L | 18.2 | |
| [GGUF](https://huggingface.co/mradermacher/Maxine-34B-stock-GGUF/resolve/main/Maxine-34B-stock.IQ4_XS.gguf) | IQ4_XS | 18.7 | |
| [GGUF](https://huggingface.co/mradermacher/Maxine-34B-stock-GGUF/resolve/main/Maxine-34B-stock.Q4_K_S.gguf) | Q4_K_S | 19.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Maxine-34B-stock-GGUF/resolve/main/Maxine-34B-stock.Q4_K_M.gguf) | Q4_K_M | 20.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Maxine-34B-stock-GGUF/resolve/main/Maxine-34B-stock.Q5_K_S.gguf) | Q5_K_S | 23.8 | |
| [GGUF](https://huggingface.co/mradermacher/Maxine-34B-stock-GGUF/resolve/main/Maxine-34B-stock.Q5_K_M.gguf) | Q5_K_M | 24.4 | |
| [GGUF](https://huggingface.co/mradermacher/Maxine-34B-stock-GGUF/resolve/main/Maxine-34B-stock.Q6_K.gguf) | Q6_K | 28.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Maxine-34B-stock-GGUF/resolve/main/Maxine-34B-stock.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):
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 -->
|
mradermacher/TableLLM-13b-GGUF | mradermacher | 2024-05-06T05:15:43Z | 129 | 0 | transformers | [
"transformers",
"gguf",
"Table",
"QA",
"Code",
"en",
"dataset:RUCKBReasoning/TableLLM-SFT",
"base_model:RUCKBReasoning/TableLLM-13b",
"base_model:quantized:RUCKBReasoning/TableLLM-13b",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T06:37:28Z | ---
base_model: RUCKBReasoning/TableLLM-13b
datasets:
- RUCKBReasoning/TableLLM-SFT
language:
- en
library_name: transformers
license: llama2
quantized_by: mradermacher
tags:
- Table
- QA
- Code
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/RUCKBReasoning/TableLLM-13b
<!-- 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/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.IQ3_XS.gguf) | IQ3_XS | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.IQ3_M.gguf) | IQ3_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.Q5_K_M.gguf) | Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.Q8_0.gguf) | Q8_0 | 13.9 | 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
## 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 -->
|
mradermacher/Turkcell-LLM-7b-v1-GGUF | mradermacher | 2024-05-06T05:15:40Z | 54 | 4 | transformers | [
"transformers",
"gguf",
"tr",
"base_model:TURKCELL/Turkcell-LLM-7b-v1",
"base_model:quantized:TURKCELL/Turkcell-LLM-7b-v1",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-05T07:04:40Z | ---
base_model: TURKCELL/Turkcell-LLM-7b-v1
language:
- tr
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/TURKCELL/Turkcell-LLM-7b-v1
<!-- 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/Turkcell-LLM-7b-v1-GGUF/resolve/main/Turkcell-LLM-7b-v1.Q2_K.gguf) | Q2_K | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Turkcell-LLM-7b-v1-GGUF/resolve/main/Turkcell-LLM-7b-v1.IQ3_XS.gguf) | IQ3_XS | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/Turkcell-LLM-7b-v1-GGUF/resolve/main/Turkcell-LLM-7b-v1.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Turkcell-LLM-7b-v1-GGUF/resolve/main/Turkcell-LLM-7b-v1.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Turkcell-LLM-7b-v1-GGUF/resolve/main/Turkcell-LLM-7b-v1.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Turkcell-LLM-7b-v1-GGUF/resolve/main/Turkcell-LLM-7b-v1.Q3_K_M.gguf) | Q3_K_M | 3.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Turkcell-LLM-7b-v1-GGUF/resolve/main/Turkcell-LLM-7b-v1.Q3_K_L.gguf) | Q3_K_L | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Turkcell-LLM-7b-v1-GGUF/resolve/main/Turkcell-LLM-7b-v1.IQ4_XS.gguf) | IQ4_XS | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Turkcell-LLM-7b-v1-GGUF/resolve/main/Turkcell-LLM-7b-v1.Q4_K_S.gguf) | Q4_K_S | 4.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Turkcell-LLM-7b-v1-GGUF/resolve/main/Turkcell-LLM-7b-v1.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Turkcell-LLM-7b-v1-GGUF/resolve/main/Turkcell-LLM-7b-v1.Q5_K_S.gguf) | Q5_K_S | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Turkcell-LLM-7b-v1-GGUF/resolve/main/Turkcell-LLM-7b-v1.Q5_K_M.gguf) | Q5_K_M | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Turkcell-LLM-7b-v1-GGUF/resolve/main/Turkcell-LLM-7b-v1.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Turkcell-LLM-7b-v1-GGUF/resolve/main/Turkcell-LLM-7b-v1.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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 -->
|
mradermacher/MaidFlameSoup-7B-GGUF | mradermacher | 2024-05-06T05:15:34Z | 21 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:nbeerbower/MaidFlameSoup-7B",
"base_model:quantized:nbeerbower/MaidFlameSoup-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T08:10:09Z | ---
base_model: nbeerbower/MaidFlameSoup-7B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/nbeerbower/MaidFlameSoup-7B
<!-- 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/MaidFlameSoup-7B-GGUF/resolve/main/MaidFlameSoup-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/MaidFlameSoup-7B-GGUF/resolve/main/MaidFlameSoup-7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/MaidFlameSoup-7B-GGUF/resolve/main/MaidFlameSoup-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/MaidFlameSoup-7B-GGUF/resolve/main/MaidFlameSoup-7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/MaidFlameSoup-7B-GGUF/resolve/main/MaidFlameSoup-7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/MaidFlameSoup-7B-GGUF/resolve/main/MaidFlameSoup-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MaidFlameSoup-7B-GGUF/resolve/main/MaidFlameSoup-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/MaidFlameSoup-7B-GGUF/resolve/main/MaidFlameSoup-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/MaidFlameSoup-7B-GGUF/resolve/main/MaidFlameSoup-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MaidFlameSoup-7B-GGUF/resolve/main/MaidFlameSoup-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MaidFlameSoup-7B-GGUF/resolve/main/MaidFlameSoup-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/MaidFlameSoup-7B-GGUF/resolve/main/MaidFlameSoup-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/MaidFlameSoup-7B-GGUF/resolve/main/MaidFlameSoup-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/MaidFlameSoup-7B-GGUF/resolve/main/MaidFlameSoup-7B.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/bophades-mistral-7B-GGUF | mradermacher | 2024-05-06T05:15:28Z | 15 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:nbeerbower/bophades-mistral-7B",
"base_model:quantized:nbeerbower/bophades-mistral-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T08:45:46Z | ---
base_model: nbeerbower/bophades-mistral-7B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/nbeerbower/bophades-mistral-7B
<!-- 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/bophades-mistral-7B-GGUF/resolve/main/bophades-mistral-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-7B-GGUF/resolve/main/bophades-mistral-7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-7B-GGUF/resolve/main/bophades-mistral-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-7B-GGUF/resolve/main/bophades-mistral-7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-7B-GGUF/resolve/main/bophades-mistral-7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-7B-GGUF/resolve/main/bophades-mistral-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-7B-GGUF/resolve/main/bophades-mistral-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-7B-GGUF/resolve/main/bophades-mistral-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-7B-GGUF/resolve/main/bophades-mistral-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-7B-GGUF/resolve/main/bophades-mistral-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-7B-GGUF/resolve/main/bophades-mistral-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-7B-GGUF/resolve/main/bophades-mistral-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-7B-GGUF/resolve/main/bophades-mistral-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-7B-GGUF/resolve/main/bophades-mistral-7B.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF | mradermacher | 2024-05-06T05:15:23Z | 2 | 0 | transformers | [
"transformers",
"gguf",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-05T09:02:52Z | ---
base_model: ParkTaeEon/Myrrh_solar_10.7b_v0.1-dpo
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ParkTaeEon/Myrrh_solar_10.7b_v0.1-dpo
<!-- 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/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.Q2_K.gguf) | Q2_K | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.IQ3_XS.gguf) | IQ3_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.Q3_K_S.gguf) | Q3_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.IQ3_S.gguf) | IQ3_S | 4.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.IQ3_M.gguf) | IQ3_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.Q3_K_L.gguf) | Q3_K_L | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.IQ4_XS.gguf) | IQ4_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.Q5_K_S.gguf) | Q5_K_S | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.Q5_K_M.gguf) | Q5_K_M | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.Q6_K.gguf) | Q6_K | 8.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.Q8_0.gguf) | Q8_0 | 11.5 | 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
## 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 -->
|
mradermacher/dragonwar-7b-alpha-GGUF | mradermacher | 2024-05-06T05:15:21Z | 14 | 0 | transformers | [
"transformers",
"gguf",
"unsloth",
"book",
"en",
"base_model:maldv/dragonwar-7b-alpha",
"base_model:quantized:maldv/dragonwar-7b-alpha",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-05T09:36:06Z | ---
base_model: maldv/dragonwar-7b-alpha
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- unsloth
- book
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/maldv/dragonwar-7b-alpha
<!-- 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/dragonwar-7b-alpha-GGUF/resolve/main/dragonwar-7b-alpha.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-alpha-GGUF/resolve/main/dragonwar-7b-alpha.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-alpha-GGUF/resolve/main/dragonwar-7b-alpha.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-alpha-GGUF/resolve/main/dragonwar-7b-alpha.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-alpha-GGUF/resolve/main/dragonwar-7b-alpha.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-alpha-GGUF/resolve/main/dragonwar-7b-alpha.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-alpha-GGUF/resolve/main/dragonwar-7b-alpha.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-alpha-GGUF/resolve/main/dragonwar-7b-alpha.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-alpha-GGUF/resolve/main/dragonwar-7b-alpha.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-alpha-GGUF/resolve/main/dragonwar-7b-alpha.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-alpha-GGUF/resolve/main/dragonwar-7b-alpha.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-alpha-GGUF/resolve/main/dragonwar-7b-alpha.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-alpha-GGUF/resolve/main/dragonwar-7b-alpha.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-alpha-GGUF/resolve/main/dragonwar-7b-alpha.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/Swallow-70b-NVE-RP-GGUF | mradermacher | 2024-05-06T05:15:12Z | 12 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"ja",
"base_model:nitky/Swallow-70b-NVE-RP",
"base_model:quantized:nitky/Swallow-70b-NVE-RP",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T09:56:33Z | ---
base_model: nitky/Swallow-70b-NVE-RP
language:
- en
- ja
library_name: transformers
license: llama2
model_type: llama
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/nitky/Swallow-70b-NVE-RP
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-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/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.Q2_K.gguf) | Q2_K | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.IQ3_XS.gguf) | IQ3_XS | 28.4 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.IQ3_S.gguf) | IQ3_S | 30.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.Q3_K_S.gguf) | Q3_K_S | 30.0 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.IQ3_M.gguf) | IQ3_M | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.Q3_K_M.gguf) | Q3_K_M | 33.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.Q3_K_L.gguf) | Q3_K_L | 36.2 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.IQ4_XS.gguf) | IQ4_XS | 37.3 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.Q4_K_S.gguf) | Q4_K_S | 39.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.Q4_K_M.gguf) | Q4_K_M | 41.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.Q5_K_S.gguf) | Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.Q5_K_M.gguf) | Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.Q6_K.gguf.part2of2) | Q6_K | 56.7 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.Q8_0.gguf.part2of2) | Q8_0 | 73.4 | 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
## 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 -->
|
mradermacher/nox-solar-10.7b-v4-kolon-ITD-5-v2.0-GGUF | mradermacher | 2024-05-06T05:15:09Z | 71 | 0 | transformers | [
"transformers",
"gguf",
"ko",
"en",
"base_model:gwonny/nox-solar-10.7b-v4-kolon-ITD-5-v2.0",
"base_model:quantized:gwonny/nox-solar-10.7b-v4-kolon-ITD-5-v2.0",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-05T10:26:41Z | ---
base_model: gwonny/nox-solar-10.7b-v4-kolon-ITD-5-v2.0
language:
- ko
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/gwonny/nox-solar-10.7b-v4-kolon-ITD-5-v2.0
<!-- 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/nox-solar-10.7b-v4-kolon-ITD-5-v2.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-ITD-5-v2.0.Q2_K.gguf) | Q2_K | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-ITD-5-v2.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-ITD-5-v2.0.IQ3_XS.gguf) | IQ3_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-ITD-5-v2.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-ITD-5-v2.0.Q3_K_S.gguf) | Q3_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-ITD-5-v2.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-ITD-5-v2.0.IQ3_S.gguf) | IQ3_S | 4.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-ITD-5-v2.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-ITD-5-v2.0.IQ3_M.gguf) | IQ3_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-ITD-5-v2.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-ITD-5-v2.0.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-ITD-5-v2.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-ITD-5-v2.0.Q3_K_L.gguf) | Q3_K_L | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-ITD-5-v2.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-ITD-5-v2.0.IQ4_XS.gguf) | IQ4_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-ITD-5-v2.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-ITD-5-v2.0.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-ITD-5-v2.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-ITD-5-v2.0.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-ITD-5-v2.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-ITD-5-v2.0.Q5_K_S.gguf) | Q5_K_S | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-ITD-5-v2.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-ITD-5-v2.0.Q5_K_M.gguf) | Q5_K_M | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-ITD-5-v2.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-ITD-5-v2.0.Q6_K.gguf) | Q6_K | 8.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-ITD-5-v2.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-ITD-5-v2.0.Q8_0.gguf) | Q8_0 | 11.5 | 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
## 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 -->
|
mradermacher/Mermaid-Yi-9B-GGUF | mradermacher | 2024-05-06T05:15:07Z | 1 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:TroyDoesAI/Mermaid-Yi-9B",
"base_model:quantized:TroyDoesAI/Mermaid-Yi-9B",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T11:26:08Z | ---
base_model: TroyDoesAI/Mermaid-Yi-9B
language:
- en
library_name: transformers
license: cc-by-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/TroyDoesAI/Mermaid-Yi-9B
<!-- 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/Mermaid-Yi-9B-GGUF/resolve/main/Mermaid-Yi-9B.Q2_K.gguf) | Q2_K | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Yi-9B-GGUF/resolve/main/Mermaid-Yi-9B.IQ3_XS.gguf) | IQ3_XS | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Yi-9B-GGUF/resolve/main/Mermaid-Yi-9B.Q3_K_S.gguf) | Q3_K_S | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Yi-9B-GGUF/resolve/main/Mermaid-Yi-9B.IQ3_S.gguf) | IQ3_S | 4.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Yi-9B-GGUF/resolve/main/Mermaid-Yi-9B.IQ3_M.gguf) | IQ3_M | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Yi-9B-GGUF/resolve/main/Mermaid-Yi-9B.Q3_K_M.gguf) | Q3_K_M | 4.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Yi-9B-GGUF/resolve/main/Mermaid-Yi-9B.Q3_K_L.gguf) | Q3_K_L | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Yi-9B-GGUF/resolve/main/Mermaid-Yi-9B.IQ4_XS.gguf) | IQ4_XS | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Yi-9B-GGUF/resolve/main/Mermaid-Yi-9B.Q4_K_S.gguf) | Q4_K_S | 5.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Yi-9B-GGUF/resolve/main/Mermaid-Yi-9B.Q4_K_M.gguf) | Q4_K_M | 5.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Yi-9B-GGUF/resolve/main/Mermaid-Yi-9B.Q5_K_S.gguf) | Q5_K_S | 6.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Yi-9B-GGUF/resolve/main/Mermaid-Yi-9B.Q5_K_M.gguf) | Q5_K_M | 6.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Yi-9B-GGUF/resolve/main/Mermaid-Yi-9B.Q6_K.gguf) | Q6_K | 7.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Yi-9B-GGUF/resolve/main/Mermaid-Yi-9B.Q8_0.gguf) | Q8_0 | 9.5 | 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
## 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 -->
|
Jaymax/llama3_FDA_qnabot_ver2-sft-test-push_ver2 | Jaymax | 2024-05-06T05:15:04Z | 82 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-05-06T05:09:58Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mradermacher/Myrrh_solar_10.7b_v0.1-GGUF | mradermacher | 2024-05-06T05:14:59Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-05T11:43:30Z | ---
base_model: ParkTaeEon/Myrrh_solar_10.7b_v0.1
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ParkTaeEon/Myrrh_solar_10.7b_v0.1
<!-- 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/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.Q2_K.gguf) | Q2_K | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.IQ3_XS.gguf) | IQ3_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.Q3_K_S.gguf) | Q3_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.IQ3_S.gguf) | IQ3_S | 4.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.IQ3_M.gguf) | IQ3_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.Q3_K_L.gguf) | Q3_K_L | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.IQ4_XS.gguf) | IQ4_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.Q5_K_S.gguf) | Q5_K_S | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.Q5_K_M.gguf) | Q5_K_M | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.Q6_K.gguf) | Q6_K | 8.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.Q8_0.gguf) | Q8_0 | 11.5 | 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
## 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 -->
|
mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF | mradermacher | 2024-05-06T05:14:57Z | 4 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"ja",
"license:llama2",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-05T12:00:20Z | ---
base_model: Aratako/Superkarakuri-lm-chat-70b-v0.1
language:
- ja
library_name: transformers
license: llama2
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Aratako/Superkarakuri-lm-chat-70b-v0.1
<!-- 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/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q2_K.gguf) | Q2_K | 25.7 | |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.IQ3_XS.gguf) | IQ3_XS | 28.6 | |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.IQ3_S.gguf) | IQ3_S | 30.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q3_K_S.gguf) | Q3_K_S | 30.2 | |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.IQ3_M.gguf) | IQ3_M | 31.2 | |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q3_K_M.gguf) | Q3_K_M | 33.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q3_K_L.gguf) | Q3_K_L | 36.4 | |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.IQ4_XS.gguf) | IQ4_XS | 37.4 | |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q4_K_S.gguf) | Q4_K_S | 39.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q4_K_M.gguf) | Q4_K_M | 41.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q5_K_S.gguf) | Q5_K_S | 47.7 | |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q5_K_M.gguf) | Q5_K_M | 49.0 | |
| [PART 1](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q6_K.gguf.part2of2) | Q6_K | 56.9 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q8_0.gguf.part2of2) | Q8_0 | 73.6 | 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
## 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 -->
|
mradermacher/pandafish-2-7b-32k-GGUF | mradermacher | 2024-05-06T05:14:48Z | 16 | 5 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"cognitivecomputations/dolphin-2.8-mistral-7b-v02",
"en",
"base_model:ichigoberry/pandafish-2-7b-32k",
"base_model:quantized:ichigoberry/pandafish-2-7b-32k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T14:05:35Z | ---
base_model: ichigoberry/pandafish-2-7b-32k
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- cognitivecomputations/dolphin-2.8-mistral-7b-v02
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ichigoberry/pandafish-2-7b-32k
<!-- 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/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF | mradermacher | 2024-05-06T05:14:46Z | 107 | 1 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"Undi95/Mistral-ClaudeLimaRP-v3-7B",
"SanjiWatsuki/Silicon-Maid-7B",
"en",
"base_model:akrads/ClaudeLimaRP-Maid-10.7B",
"base_model:quantized:akrads/ClaudeLimaRP-Maid-10.7B",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T14:16:23Z | ---
base_model: akrads/ClaudeLimaRP-Maid-10.7B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- Undi95/Mistral-ClaudeLimaRP-v3-7B
- SanjiWatsuki/Silicon-Maid-7B
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/akrads/ClaudeLimaRP-Maid-10.7B
<!-- 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/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.Q2_K.gguf) | Q2_K | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.IQ3_XS.gguf) | IQ3_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.Q3_K_S.gguf) | Q3_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.IQ3_S.gguf) | IQ3_S | 4.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.IQ3_M.gguf) | IQ3_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.Q3_K_L.gguf) | Q3_K_L | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.IQ4_XS.gguf) | IQ4_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.Q5_K_S.gguf) | Q5_K_S | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.Q5_K_M.gguf) | Q5_K_M | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.Q6_K.gguf) | Q6_K | 8.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.Q8_0.gguf) | Q8_0 | 11.5 | 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
## 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 -->
|
mradermacher/Swallow-70b-NVE-RP-i1-GGUF | mradermacher | 2024-05-06T05:14:43Z | 99 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"ja",
"base_model:nitky/Swallow-70b-NVE-RP",
"base_model:quantized:nitky/Swallow-70b-NVE-RP",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T14:20:52Z | ---
base_model: nitky/Swallow-70b-NVE-RP
language:
- en
- ja
library_name: transformers
license: llama2
model_type: llama
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/nitky/Swallow-70b-NVE-RP
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-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/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ1_S.gguf) | i1-IQ1_S | 14.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ1_M.gguf) | i1-IQ1_M | 16.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 18.4 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ2_XS.gguf) | i1-IQ2_XS | 20.4 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ2_S.gguf) | i1-IQ2_S | 21.5 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ2_M.gguf) | i1-IQ2_M | 23.3 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q2_K.gguf) | i1-Q2_K | 25.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 26.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ3_XS.gguf) | i1-IQ3_XS | 28.4 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ3_S.gguf) | i1-IQ3_S | 30.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q3_K_S.gguf) | i1-Q3_K_S | 30.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ3_M.gguf) | i1-IQ3_M | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q3_K_M.gguf) | i1-Q3_K_M | 33.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q3_K_L.gguf) | i1-Q3_K_L | 36.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ4_XS.gguf) | i1-IQ4_XS | 36.9 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q4_0.gguf) | i1-Q4_0 | 39.1 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q4_K_S.gguf) | i1-Q4_K_S | 39.3 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q4_K_M.gguf) | i1-Q4_K_M | 41.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q5_K_S.gguf) | i1-Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q5_K_M.gguf) | i1-Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 56.7 | practically like static Q6_K |
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
## 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 -->
|
mradermacher/anarchy-solar-10B-v1-GGUF | mradermacher | 2024-05-06T05:14:37Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"ko",
"base_model:moondriller/anarchy-solar-10B-v1",
"base_model:quantized:moondriller/anarchy-solar-10B-v1",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T15:34:15Z | ---
base_model: moondriller/anarchy-solar-10B-v1
language:
- ko
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/moondriller/anarchy-solar-10B-v1
<!-- 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/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.Q2_K.gguf) | Q2_K | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.IQ3_XS.gguf) | IQ3_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.Q3_K_S.gguf) | Q3_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.IQ3_S.gguf) | IQ3_S | 4.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.IQ3_M.gguf) | IQ3_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.Q3_K_L.gguf) | Q3_K_L | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.IQ4_XS.gguf) | IQ4_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.Q5_K_S.gguf) | Q5_K_S | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.Q5_K_M.gguf) | Q5_K_M | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.Q6_K.gguf) | Q6_K | 8.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.Q8_0.gguf) | Q8_0 | 11.5 | 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
## 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 -->
|
mradermacher/anarchy-llama2-13B-v2-GGUF | mradermacher | 2024-05-06T05:14:30Z | 3 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:moondriller/anarchy-llama2-13B-v2",
"base_model:quantized:moondriller/anarchy-llama2-13B-v2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T16:58:16Z | ---
base_model: moondriller/anarchy-llama2-13B-v2
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/moondriller/anarchy-llama2-13B-v2
<!-- 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/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.IQ3_XS.gguf) | IQ3_XS | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.IQ3_S.gguf) | IQ3_S | 5.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.Q3_K_S.gguf) | Q3_K_S | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.IQ3_M.gguf) | IQ3_M | 6.2 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.Q3_K_M.gguf) | Q3_K_M | 6.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.Q3_K_L.gguf) | Q3_K_L | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.IQ4_XS.gguf) | IQ4_XS | 7.2 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.Q4_K_S.gguf) | Q4_K_S | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.Q4_K_M.gguf) | Q4_K_M | 8.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.Q5_K_S.gguf) | Q5_K_S | 9.2 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.Q5_K_M.gguf) | Q5_K_M | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.Q6_K.gguf) | Q6_K | 10.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.Q8_0.gguf) | Q8_0 | 14.1 | 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
## 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 -->
|
mradermacher/Mergerix-7b-v0.5-GGUF | mradermacher | 2024-05-06T05:14:27Z | 5 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"automerger/YamshadowExperiment28-7B",
"automerger/PasticheInex12-7B",
"en",
"base_model:MiniMoog/Mergerix-7b-v0.5",
"base_model:quantized:MiniMoog/Mergerix-7b-v0.5",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T18:32:48Z | ---
base_model: MiniMoog/Mergerix-7b-v0.5
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- automerger/YamshadowExperiment28-7B
- automerger/PasticheInex12-7B
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/MiniMoog/Mergerix-7b-v0.5
<!-- 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/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/H4na-7B-v0.1-GGUF | mradermacher | 2024-05-06T05:14:20Z | 23 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"sft",
"en",
"base_model:Smuggling1710/H4na-7B-v0.1",
"base_model:quantized:Smuggling1710/H4na-7B-v0.1",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T19:53:00Z | ---
base_model: Smuggling1710/H4na-7B-v0.1
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Smuggling1710/H4na-7B-v0.1
<!-- 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/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/13B-HyperMantis-GGUF | mradermacher | 2024-05-06T05:14:16Z | 97 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"alpaca",
"vicuna",
"mix",
"merge",
"model merge",
"roleplay",
"chat",
"instruct",
"en",
"base_model:digitous/13B-HyperMantis",
"base_model:quantized:digitous/13B-HyperMantis",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T20:04:53Z | ---
base_model: digitous/13B-HyperMantis
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
tags:
- llama
- alpaca
- vicuna
- mix
- merge
- model merge
- roleplay
- chat
- instruct
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/digitous/13B-HyperMantis
<!-- 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/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.IQ3_XS.gguf) | IQ3_XS | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.IQ3_M.gguf) | IQ3_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.Q5_K_M.gguf) | Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.Q8_0.gguf) | Q8_0 | 13.9 | 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
## 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 -->
|
mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-GGUF | mradermacher | 2024-05-06T05:13:56Z | 3 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"fr",
"it",
"de",
"es",
"en",
"base_model:Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled",
"base_model:quantized:Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-05T21:19:21Z | ---
base_model: Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled
language:
- fr
- it
- de
- es
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-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/Mixtral-8x7B-Instruct-v0.1-upscaled-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1-upscaled.Q2_K.gguf) | Q2_K | 30.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1-upscaled.IQ3_XS.gguf) | IQ3_XS | 33.7 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1-upscaled.IQ3_S.gguf) | IQ3_S | 35.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1-upscaled.Q3_K_S.gguf) | Q3_K_S | 35.7 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1-upscaled.IQ3_M.gguf) | IQ3_M | 37.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1-upscaled.Q3_K_M.gguf) | Q3_K_M | 39.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1-upscaled.Q3_K_L.gguf) | Q3_K_L | 42.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1-upscaled.IQ4_XS.gguf) | IQ4_XS | 44.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1-upscaled.Q4_K_S.gguf) | Q4_K_S | 46.8 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1-upscaled.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1-upscaled.Q4_K_M.gguf.part2of2) | Q4_K_M | 49.7 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1-upscaled.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1-upscaled.Q5_K_S.gguf.part2of2) | Q5_K_S | 56.4 | |
| [PART 1](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1-upscaled.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1-upscaled.Q5_K_M.gguf.part2of2) | Q5_K_M | 58.1 | |
| [PART 1](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1-upscaled.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1-upscaled.Q6_K.gguf.part2of2) | Q6_K | 67.1 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1-upscaled.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1-upscaled.Q8_0.gguf.part2of2) | Q8_0 | 86.7 | 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
## 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 -->
|
mradermacher/13B-Chimera-GGUF | mradermacher | 2024-05-06T05:13:49Z | 34 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"cot",
"vicuna",
"uncensored",
"merge",
"mix",
"gptq",
"en",
"base_model:digitous/13B-Chimera",
"base_model:quantized:digitous/13B-Chimera",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T23:11:33Z | ---
base_model: digitous/13B-Chimera
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- llama
- cot
- vicuna
- uncensored
- merge
- mix
- gptq
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/digitous/13B-Chimera
<!-- 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/13B-Chimera-GGUF/resolve/main/13B-Chimera.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.IQ3_XS.gguf) | IQ3_XS | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.IQ3_M.gguf) | IQ3_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.Q5_K_M.gguf) | Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.Q8_0.gguf) | Q8_0 | 13.9 | 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
## 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 -->
|
mradermacher/LemonadeRP-4.5.3-11B-GGUF | mradermacher | 2024-05-06T05:13:46Z | 6 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:mpasila/LemonadeRP-4.5.3-11B",
"base_model:quantized:mpasila/LemonadeRP-4.5.3-11B",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T00:30:17Z | ---
base_model: mpasila/LemonadeRP-4.5.3-11B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/mpasila/LemonadeRP-4.5.3-11B
<!-- 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/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.Q2_K.gguf) | Q2_K | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.IQ3_XS.gguf) | IQ3_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.Q3_K_S.gguf) | Q3_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.IQ3_S.gguf) | IQ3_S | 4.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.IQ3_M.gguf) | IQ3_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.Q3_K_L.gguf) | Q3_K_L | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.IQ4_XS.gguf) | IQ4_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.Q5_K_S.gguf) | Q5_K_S | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.Q5_K_M.gguf) | Q5_K_M | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.Q6_K.gguf) | Q6_K | 8.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.Q8_0.gguf) | Q8_0 | 11.5 | 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
## 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 -->
|
mradermacher/WizardLM-30B-V1.0-i1-GGUF | mradermacher | 2024-05-06T05:13:43Z | 13 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:WizardLM/WizardLM-30B-V1.0",
"base_model:quantized:WizardLM/WizardLM-30B-V1.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T01:24:45Z | ---
base_model: WizardLM/WizardLM-30B-V1.0
language:
- en
library_name: transformers
no_imatrix: 'GGML_ASSERT: llama.cpp/ggml-quants.c:12166: besti1 >= 0 && besti2 >=
0 && best_k >= 0'
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/WizardLM/WizardLM-30B-V1.0
**No IQ1\* quants as llama.cpp is crashing when trying to generate it**
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/WizardLM-30B-V1.0-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/WizardLM-30B-V1.0-i1-GGUF/resolve/main/WizardLM-30B-V1.0.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-i1-GGUF/resolve/main/WizardLM-30B-V1.0.i1-IQ2_XS.gguf) | i1-IQ2_XS | 9.7 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-i1-GGUF/resolve/main/WizardLM-30B-V1.0.i1-IQ2_S.gguf) | i1-IQ2_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-i1-GGUF/resolve/main/WizardLM-30B-V1.0.i1-IQ2_M.gguf) | i1-IQ2_M | 11.3 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-i1-GGUF/resolve/main/WizardLM-30B-V1.0.i1-Q2_K.gguf) | i1-Q2_K | 12.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-i1-GGUF/resolve/main/WizardLM-30B-V1.0.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-i1-GGUF/resolve/main/WizardLM-30B-V1.0.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-i1-GGUF/resolve/main/WizardLM-30B-V1.0.i1-IQ3_S.gguf) | i1-IQ3_S | 14.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-i1-GGUF/resolve/main/WizardLM-30B-V1.0.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.2 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-i1-GGUF/resolve/main/WizardLM-30B-V1.0.i1-IQ3_M.gguf) | i1-IQ3_M | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-i1-GGUF/resolve/main/WizardLM-30B-V1.0.i1-Q3_K_M.gguf) | i1-Q3_K_M | 15.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-i1-GGUF/resolve/main/WizardLM-30B-V1.0.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-i1-GGUF/resolve/main/WizardLM-30B-V1.0.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-i1-GGUF/resolve/main/WizardLM-30B-V1.0.i1-Q4_0.gguf) | i1-Q4_0 | 18.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-i1-GGUF/resolve/main/WizardLM-30B-V1.0.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-i1-GGUF/resolve/main/WizardLM-30B-V1.0.i1-Q4_K_M.gguf) | i1-Q4_K_M | 19.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-i1-GGUF/resolve/main/WizardLM-30B-V1.0.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.5 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-i1-GGUF/resolve/main/WizardLM-30B-V1.0.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.1 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-i1-GGUF/resolve/main/WizardLM-30B-V1.0.i1-Q6_K.gguf) | i1-Q6_K | 26.8 | practically like static Q6_K |
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
## 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 -->
|
mradermacher/FNCARLplus-7b-GGUF | mradermacher | 2024-05-06T05:13:36Z | 90 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:jambroz/FNCARLplus-7b",
"base_model:quantized:jambroz/FNCARLplus-7b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-06T02:16:58Z | ---
base_model: jambroz/FNCARLplus-7b
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/jambroz/FNCARLplus-7b
<!-- 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/FNCARLplus-7b-GGUF/resolve/main/FNCARLplus-7b.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARLplus-7b-GGUF/resolve/main/FNCARLplus-7b.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARLplus-7b-GGUF/resolve/main/FNCARLplus-7b.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARLplus-7b-GGUF/resolve/main/FNCARLplus-7b.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/FNCARLplus-7b-GGUF/resolve/main/FNCARLplus-7b.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARLplus-7b-GGUF/resolve/main/FNCARLplus-7b.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/FNCARLplus-7b-GGUF/resolve/main/FNCARLplus-7b.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARLplus-7b-GGUF/resolve/main/FNCARLplus-7b.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARLplus-7b-GGUF/resolve/main/FNCARLplus-7b.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/FNCARLplus-7b-GGUF/resolve/main/FNCARLplus-7b.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/FNCARLplus-7b-GGUF/resolve/main/FNCARLplus-7b.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARLplus-7b-GGUF/resolve/main/FNCARLplus-7b.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARLplus-7b-GGUF/resolve/main/FNCARLplus-7b.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/FNCARLplus-7b-GGUF/resolve/main/FNCARLplus-7b.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF | mradermacher | 2024-05-06T05:13:24Z | 4 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:kaist-ai/prometheus-8x7b-v2.0-1-pp",
"base_model:quantized:kaist-ai/prometheus-8x7b-v2.0-1-pp",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-06T05:27:51Z | ---
base_model: kaist-ai/prometheus-8x7b-v2.0-1-pp
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/kaist-ai/prometheus-8x7b-v2.0-1-pp
<!-- 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/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q2_K.gguf) | Q2_K | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.IQ3_XS.gguf) | IQ3_XS | 19.4 | |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.IQ3_S.gguf) | IQ3_S | 20.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q3_K_S.gguf) | Q3_K_S | 20.5 | |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.IQ3_M.gguf) | IQ3_M | 21.5 | |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q3_K_M.gguf) | Q3_K_M | 22.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q3_K_L.gguf) | Q3_K_L | 24.3 | |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.IQ4_XS.gguf) | IQ4_XS | 25.5 | |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q4_K_S.gguf) | Q4_K_S | 26.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q4_K_M.gguf) | Q4_K_M | 28.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q5_K_S.gguf) | Q5_K_S | 32.3 | |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q5_K_M.gguf) | Q5_K_M | 33.3 | |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q6_K.gguf) | Q6_K | 38.5 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q8_0.gguf.part2of2) | Q8_0 | 49.7 | 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
## 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 -->
|
mradermacher/pandafish-3-7B-32k-GGUF | mradermacher | 2024-05-06T05:13:16Z | 1 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:ichigoberry/pandafish-3-7B-32k",
"base_model:quantized:ichigoberry/pandafish-3-7B-32k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T06:14:55Z | ---
base_model: ichigoberry/pandafish-3-7B-32k
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ichigoberry/pandafish-3-7B-32k
<!-- 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/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/FNCARL-7b-dpo-GGUF | mradermacher | 2024-05-06T05:12:49Z | 4 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:jambroz/FNCARL-7b-dpo",
"base_model:quantized:jambroz/FNCARL-7b-dpo",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-06T09:46:29Z | ---
base_model: jambroz/FNCARL-7b-dpo
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/jambroz/FNCARL-7b-dpo
<!-- 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/FNCARL-7b-dpo-GGUF/resolve/main/FNCARL-7b-dpo.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-dpo-GGUF/resolve/main/FNCARL-7b-dpo.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-dpo-GGUF/resolve/main/FNCARL-7b-dpo.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-dpo-GGUF/resolve/main/FNCARL-7b-dpo.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-dpo-GGUF/resolve/main/FNCARL-7b-dpo.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-dpo-GGUF/resolve/main/FNCARL-7b-dpo.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-dpo-GGUF/resolve/main/FNCARL-7b-dpo.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-dpo-GGUF/resolve/main/FNCARL-7b-dpo.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-dpo-GGUF/resolve/main/FNCARL-7b-dpo.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-dpo-GGUF/resolve/main/FNCARL-7b-dpo.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-dpo-GGUF/resolve/main/FNCARL-7b-dpo.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-dpo-GGUF/resolve/main/FNCARL-7b-dpo.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-dpo-GGUF/resolve/main/FNCARL-7b-dpo.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/FNCARL-7b-dpo-GGUF/resolve/main/FNCARL-7b-dpo.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/Pioneer-2x7B-GGUF | mradermacher | 2024-05-06T05:12:46Z | 78 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:hibana2077/Pioneer-2x7B",
"base_model:quantized:hibana2077/Pioneer-2x7B",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T10:24:47Z | ---
base_model: hibana2077/Pioneer-2x7B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/hibana2077/Pioneer-2x7B
<!-- 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/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/Wittgenbot-7B-GGUF | mradermacher | 2024-05-06T05:12:38Z | 6 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:descartesevildemon/Wittgenbot-7B",
"base_model:quantized:descartesevildemon/Wittgenbot-7B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-06T10:54:34Z | ---
base_model: descartesevildemon/Wittgenbot-7B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/descartesevildemon/Wittgenbot-7B
<!-- 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/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF | mradermacher | 2024-05-06T05:12:26Z | 1 | 0 | transformers | [
"transformers",
"gguf",
"SkillEnhanced",
"mistral",
"en",
"base_model:HachiML/Swallow-MS-7b-v0.1-ChatMathSkill",
"base_model:quantized:HachiML/Swallow-MS-7b-v0.1-ChatMathSkill",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T11:24:30Z | ---
base_model: HachiML/Swallow-MS-7b-v0.1-ChatMathSkill
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- SkillEnhanced
- mistral
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/HachiML/Swallow-MS-7b-v0.1-ChatMathSkill
<!-- 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/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.Q2_K.gguf) | Q2_K | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.IQ3_XS.gguf) | IQ3_XS | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.Q3_K_M.gguf) | Q3_K_M | 3.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.Q3_K_L.gguf) | Q3_K_L | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.IQ4_XS.gguf) | IQ4_XS | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.Q4_K_S.gguf) | Q4_K_S | 4.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.Q5_K_S.gguf) | Q5_K_S | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.Q5_K_M.gguf) | Q5_K_M | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.Q6_K.gguf) | Q6_K | 6.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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 -->
|
jeongmi/solar_insta_chai_80_final | jeongmi | 2024-05-06T05:12:10Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-04-22T04:05:18Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
mradermacher/v1olet_merged_dpo_7B-GGUF | mradermacher | 2024-05-06T05:12:04Z | 35 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:v1olet/v1olet_merged_dpo_7B",
"base_model:quantized:v1olet/v1olet_merged_dpo_7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T15:17:18Z | ---
base_model: v1olet/v1olet_merged_dpo_7B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/v1olet/v1olet_merged_dpo_7B
<!-- 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/v1olet_merged_dpo_7B-GGUF/resolve/main/v1olet_merged_dpo_7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/v1olet_merged_dpo_7B-GGUF/resolve/main/v1olet_merged_dpo_7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/v1olet_merged_dpo_7B-GGUF/resolve/main/v1olet_merged_dpo_7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/v1olet_merged_dpo_7B-GGUF/resolve/main/v1olet_merged_dpo_7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/v1olet_merged_dpo_7B-GGUF/resolve/main/v1olet_merged_dpo_7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/v1olet_merged_dpo_7B-GGUF/resolve/main/v1olet_merged_dpo_7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/v1olet_merged_dpo_7B-GGUF/resolve/main/v1olet_merged_dpo_7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/v1olet_merged_dpo_7B-GGUF/resolve/main/v1olet_merged_dpo_7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/v1olet_merged_dpo_7B-GGUF/resolve/main/v1olet_merged_dpo_7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/v1olet_merged_dpo_7B-GGUF/resolve/main/v1olet_merged_dpo_7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/v1olet_merged_dpo_7B-GGUF/resolve/main/v1olet_merged_dpo_7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/v1olet_merged_dpo_7B-GGUF/resolve/main/v1olet_merged_dpo_7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/v1olet_merged_dpo_7B-GGUF/resolve/main/v1olet_merged_dpo_7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/v1olet_merged_dpo_7B-GGUF/resolve/main/v1olet_merged_dpo_7B.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/Alpaca-elina-65b-GGUF | mradermacher | 2024-05-06T05:11:45Z | 5 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Aeala/Alpaca-elina-65b",
"base_model:quantized:Aeala/Alpaca-elina-65b",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T19:05:16Z | ---
base_model: Aeala/Alpaca-elina-65b
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Aeala/Alpaca-elina-65b
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Alpaca-elina-65b-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/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q2_K.gguf) | Q2_K | 24.2 | |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.IQ3_XS.gguf) | IQ3_XS | 26.7 | |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.IQ3_S.gguf) | IQ3_S | 28.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q3_K_S.gguf) | Q3_K_S | 28.3 | |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.IQ3_M.gguf) | IQ3_M | 29.9 | |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q3_K_M.gguf) | Q3_K_M | 31.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q3_K_L.gguf) | Q3_K_L | 34.7 | |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.IQ4_XS.gguf) | IQ4_XS | 35.1 | |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q4_K_S.gguf) | Q4_K_S | 37.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q4_K_M.gguf) | Q4_K_M | 39.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q5_K_S.gguf) | Q5_K_S | 45.0 | |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q5_K_M.gguf) | Q5_K_M | 46.3 | |
| [PART 1](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q6_K.gguf.part2of2) | Q6_K | 53.7 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q8_0.gguf.part2of2) | Q8_0 | 69.5 | 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
## 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 -->
|
mradermacher/WinterGoddess-1.4x-70B-L2-GGUF | mradermacher | 2024-05-06T05:11:42Z | 2 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Sao10K/WinterGoddess-1.4x-70B-L2",
"base_model:quantized:Sao10K/WinterGoddess-1.4x-70B-L2",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T19:40:17Z | ---
base_model: Sao10K/WinterGoddess-1.4x-70B-L2
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Sao10K/WinterGoddess-1.4x-70B-L2
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-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/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q2_K.gguf) | Q2_K | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.IQ3_XS.gguf) | IQ3_XS | 28.4 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.IQ3_S.gguf) | IQ3_S | 30.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q3_K_S.gguf) | Q3_K_S | 30.0 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.IQ3_M.gguf) | IQ3_M | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q3_K_M.gguf) | Q3_K_M | 33.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q3_K_L.gguf) | Q3_K_L | 36.2 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.IQ4_XS.gguf) | IQ4_XS | 37.3 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q4_K_S.gguf) | Q4_K_S | 39.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q4_K_M.gguf) | Q4_K_M | 41.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q5_K_S.gguf) | Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q5_K_M.gguf) | Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q6_K.gguf.part2of2) | Q6_K | 56.7 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q8_0.gguf.part2of2) | Q8_0 | 73.4 | 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
## 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 -->
|
mradermacher/Mermaid_13B-GGUF | mradermacher | 2024-05-06T05:11:29Z | 118 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:TroyDoesAI/Mermaid_13B",
"base_model:quantized:TroyDoesAI/Mermaid_13B",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T22:03:19Z | ---
base_model: TroyDoesAI/Mermaid_13B
language:
- en
library_name: transformers
license: cc-by-nc-sa-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/TroyDoesAI/Mermaid_13B
<!-- 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/Mermaid_13B-GGUF/resolve/main/Mermaid_13B.Q2_K.gguf) | Q2_K | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_13B-GGUF/resolve/main/Mermaid_13B.IQ3_XS.gguf) | IQ3_XS | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_13B-GGUF/resolve/main/Mermaid_13B.Q3_K_S.gguf) | Q3_K_S | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_13B-GGUF/resolve/main/Mermaid_13B.IQ3_S.gguf) | IQ3_S | 6.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_13B-GGUF/resolve/main/Mermaid_13B.IQ3_M.gguf) | IQ3_M | 6.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_13B-GGUF/resolve/main/Mermaid_13B.Q3_K_M.gguf) | Q3_K_M | 6.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_13B-GGUF/resolve/main/Mermaid_13B.Q3_K_L.gguf) | Q3_K_L | 7.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_13B-GGUF/resolve/main/Mermaid_13B.IQ4_XS.gguf) | IQ4_XS | 7.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_13B-GGUF/resolve/main/Mermaid_13B.Q4_K_S.gguf) | Q4_K_S | 7.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_13B-GGUF/resolve/main/Mermaid_13B.Q4_K_M.gguf) | Q4_K_M | 8.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_13B-GGUF/resolve/main/Mermaid_13B.Q5_K_S.gguf) | Q5_K_S | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_13B-GGUF/resolve/main/Mermaid_13B.Q5_K_M.gguf) | Q5_K_M | 9.7 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_13B-GGUF/resolve/main/Mermaid_13B.Q6_K.gguf) | Q6_K | 11.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_13B-GGUF/resolve/main/Mermaid_13B.Q8_0.gguf) | Q8_0 | 14.4 | 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
## 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 -->
|
mradermacher/PandafishHeatherReReloaded-GGUF | mradermacher | 2024-05-06T05:11:22Z | 131 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"MysticFoxMagic/HeatherSpell-7b",
"ichigoberry/pandafish-2-7b-32k",
"en",
"base_model:MysticFoxMagic/PandafishHeatherReReloaded",
"base_model:quantized:MysticFoxMagic/PandafishHeatherReReloaded",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T00:10:05Z | ---
base_model: MysticFoxMagic/PandafishHeatherReReloaded
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- MysticFoxMagic/HeatherSpell-7b
- ichigoberry/pandafish-2-7b-32k
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/MysticFoxMagic/PandafishHeatherReReloaded
<!-- 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/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
CodeTriad/mistral-base-finetune-15000-unique-second | CodeTriad | 2024-05-06T05:11:21Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"region:us"
] | null | 2024-05-06T05:11:05Z | ---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0 |
mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF | mradermacher | 2024-05-06T05:11:19Z | 11 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Sao10K/WinterGoddess-1.4x-70B-L2",
"base_model:quantized:Sao10K/WinterGoddess-1.4x-70B-L2",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T00:23:15Z | ---
base_model: Sao10K/WinterGoddess-1.4x-70B-L2
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/Sao10K/WinterGoddess-1.4x-70B-L2
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-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/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-IQ1_S.gguf) | i1-IQ1_S | 14.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-IQ1_M.gguf) | i1-IQ1_M | 16.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 18.4 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 20.4 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-IQ2_S.gguf) | i1-IQ2_S | 21.5 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-IQ2_M.gguf) | i1-IQ2_M | 23.3 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-Q2_K.gguf) | i1-Q2_K | 25.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 26.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 28.4 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-IQ3_S.gguf) | i1-IQ3_S | 30.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 30.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-IQ3_M.gguf) | i1-IQ3_M | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 33.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 36.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 36.9 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-Q4_0.gguf) | i1-Q4_0 | 39.1 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 39.3 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 41.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 56.7 | practically like static Q6_K |
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
## 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 -->
|
harshraj/phi-1_5_hinglish_text_pretrained | harshraj | 2024-05-06T05:11:18Z | 168 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-06T04:31:50Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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mradermacher/XunziALLM-GGUF | mradermacher | 2024-05-06T05:11:17Z | 69 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:ccwu0918/XunziALLM",
"base_model:quantized:ccwu0918/XunziALLM",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T01:10:14Z | ---
base_model: ccwu0918/XunziALLM
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ccwu0918/XunziALLM
<!-- 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/XunziALLM-GGUF/resolve/main/XunziALLM.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/XunziALLM-GGUF/resolve/main/XunziALLM.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/XunziALLM-GGUF/resolve/main/XunziALLM.IQ3_S.gguf) | IQ3_S | 3.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/XunziALLM-GGUF/resolve/main/XunziALLM.Q3_K_S.gguf) | Q3_K_S | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/XunziALLM-GGUF/resolve/main/XunziALLM.IQ3_M.gguf) | IQ3_M | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/XunziALLM-GGUF/resolve/main/XunziALLM.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/XunziALLM-GGUF/resolve/main/XunziALLM.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/XunziALLM-GGUF/resolve/main/XunziALLM.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/XunziALLM-GGUF/resolve/main/XunziALLM.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/XunziALLM-GGUF/resolve/main/XunziALLM.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/XunziALLM-GGUF/resolve/main/XunziALLM.Q5_K_S.gguf) | Q5_K_S | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/XunziALLM-GGUF/resolve/main/XunziALLM.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/XunziALLM-GGUF/resolve/main/XunziALLM.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/XunziALLM-GGUF/resolve/main/XunziALLM.Q8_0.gguf) | Q8_0 | 8.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/XunziALLM-GGUF/resolve/main/XunziALLM.SOURCE.gguf) | SOURCE | 15.5 | source gguf, only provided when it was hard to come by |
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
## 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 -->
|
mradermacher/OpenDolphin-7B-slerp-GGUF | mradermacher | 2024-05-06T05:11:13Z | 16 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"macadeliccc/Mistral-7B-v0.2-OpenHermes",
"cognitivecomputations/dolphin-2.8-mistral-7b-v02",
"en",
"base_model:WesPro/OpenDolphin-7B-slerp",
"base_model:quantized:WesPro/OpenDolphin-7B-slerp",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-07T01:27:55Z | ---
base_model: WesPro/OpenDolphin-7B-slerp
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- macadeliccc/Mistral-7B-v0.2-OpenHermes
- cognitivecomputations/dolphin-2.8-mistral-7b-v02
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/WesPro/OpenDolphin-7B-slerp
<!-- 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/OpenDolphin-7B-slerp-GGUF/resolve/main/OpenDolphin-7B-slerp.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/OpenDolphin-7B-slerp-GGUF/resolve/main/OpenDolphin-7B-slerp.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/OpenDolphin-7B-slerp-GGUF/resolve/main/OpenDolphin-7B-slerp.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/OpenDolphin-7B-slerp-GGUF/resolve/main/OpenDolphin-7B-slerp.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/OpenDolphin-7B-slerp-GGUF/resolve/main/OpenDolphin-7B-slerp.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/OpenDolphin-7B-slerp-GGUF/resolve/main/OpenDolphin-7B-slerp.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/OpenDolphin-7B-slerp-GGUF/resolve/main/OpenDolphin-7B-slerp.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/OpenDolphin-7B-slerp-GGUF/resolve/main/OpenDolphin-7B-slerp.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/OpenDolphin-7B-slerp-GGUF/resolve/main/OpenDolphin-7B-slerp.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OpenDolphin-7B-slerp-GGUF/resolve/main/OpenDolphin-7B-slerp.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OpenDolphin-7B-slerp-GGUF/resolve/main/OpenDolphin-7B-slerp.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/OpenDolphin-7B-slerp-GGUF/resolve/main/OpenDolphin-7B-slerp.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/OpenDolphin-7B-slerp-GGUF/resolve/main/OpenDolphin-7B-slerp.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/OpenDolphin-7B-slerp-GGUF/resolve/main/OpenDolphin-7B-slerp.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/PandafishHeatherReloaded-GGUF | mradermacher | 2024-05-06T05:11:11Z | 89 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"ichigoberry/pandafish-dt-7b",
"MysticFoxMagic/HeatherSpell-7b",
"en",
"base_model:MysticFoxMagic/PandafishHeatherReloaded",
"base_model:quantized:MysticFoxMagic/PandafishHeatherReloaded",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T01:32:30Z | ---
base_model: MysticFoxMagic/PandafishHeatherReloaded
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- ichigoberry/pandafish-dt-7b
- MysticFoxMagic/HeatherSpell-7b
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/MysticFoxMagic/PandafishHeatherReloaded
<!-- 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/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/Tess-72B-v1.5b-GGUF | mradermacher | 2024-05-06T05:11:02Z | 1 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:migtissera/Tess-72B-v1.5b",
"base_model:quantized:migtissera/Tess-72B-v1.5b",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T03:47:12Z | ---
base_model: migtissera/Tess-72B-v1.5b
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/Qwen/Qwen-72B/blob/main/LICENSE
license_name: qwen-72b-licence
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/migtissera/Tess-72B-v1.5b
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Tess-72B-v1.5b-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/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q2_K.gguf) | Q2_K | 27.2 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.IQ3_XS.gguf) | IQ3_XS | 30.0 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.IQ3_S.gguf) | IQ3_S | 31.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q3_K_S.gguf) | Q3_K_S | 31.7 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.IQ3_M.gguf) | IQ3_M | 33.4 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q3_K_M.gguf) | Q3_K_M | 35.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q3_K_L.gguf) | Q3_K_L | 38.6 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.IQ4_XS.gguf) | IQ4_XS | 39.2 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q4_K_S.gguf) | Q4_K_S | 41.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q4_K_M.gguf) | Q4_K_M | 43.9 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q5_K_S.gguf.part2of2) | Q5_K_S | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q5_K_M.gguf.part2of2) | Q5_K_M | 51.4 | |
| [PART 1](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q6_K.gguf.part2of2) | Q6_K | 59.4 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q8_0.gguf.part2of2) | Q8_0 | 76.9 | 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
## 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 -->
|
mradermacher/Limitless-GGUF | mradermacher | 2024-05-06T05:10:56Z | 116 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:alkahestry/Limitless",
"base_model:quantized:alkahestry/Limitless",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T04:22:23Z | ---
base_model: alkahestry/Limitless
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/alkahestry/Limitless
<!-- 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/Limitless-GGUF/resolve/main/Limitless.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/WinterGoddess-1.4x-70b-32k-GGUF | mradermacher | 2024-05-06T05:10:33Z | 41 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:ChuckMcSneed/WinterGoddess-1.4x-70b-32k",
"base_model:quantized:ChuckMcSneed/WinterGoddess-1.4x-70b-32k",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T08:19:24Z | ---
base_model: ChuckMcSneed/WinterGoddess-1.4x-70b-32k
language:
- en
library_name: transformers
license: llama2
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ChuckMcSneed/WinterGoddess-1.4x-70b-32k
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-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/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q2_K.gguf) | Q2_K | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.IQ3_XS.gguf) | IQ3_XS | 28.4 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.IQ3_S.gguf) | IQ3_S | 30.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q3_K_S.gguf) | Q3_K_S | 30.0 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.IQ3_M.gguf) | IQ3_M | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q3_K_M.gguf) | Q3_K_M | 33.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q3_K_L.gguf) | Q3_K_L | 36.2 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.IQ4_XS.gguf) | IQ4_XS | 37.3 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q4_K_S.gguf) | Q4_K_S | 39.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q4_K_M.gguf) | Q4_K_M | 41.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q5_K_S.gguf) | Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q5_K_M.gguf) | Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q6_K.gguf.part2of2) | Q6_K | 56.7 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q8_0.gguf.part2of2) | Q8_0 | 73.4 | 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
## 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 -->
|
mradermacher/Tess-34B-v1.5b-i1-GGUF | mradermacher | 2024-05-06T05:10:23Z | 29 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:migtissera/Tess-34B-v1.5b",
"base_model:quantized:migtissera/Tess-34B-v1.5b",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T10:33:11Z | ---
base_model: migtissera/Tess-34B-v1.5b
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
license_name: yi-34b
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/migtissera/Tess-34B-v1.5b
**This uses only 40k tokens of my standard set, as the model overflowed with more.**
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Tess-34B-v1.5b-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/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-IQ1_S.gguf) | i1-IQ1_S | 7.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-IQ1_M.gguf) | i1-IQ1_M | 8.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-IQ2_S.gguf) | i1-IQ2_S | 11.0 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-IQ2_M.gguf) | i1-IQ2_M | 11.9 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-Q2_K.gguf) | i1-Q2_K | 12.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 13.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 14.3 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 15.1 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-IQ3_S.gguf) | i1-IQ3_S | 15.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-IQ3_M.gguf) | i1-IQ3_M | 15.7 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 18.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 18.6 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-Q4_0.gguf) | i1-Q4_0 | 19.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 19.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 23.8 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 24.4 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-34B-v1.5b-i1-GGUF/resolve/main/Tess-34B-v1.5b.i1-Q6_K.gguf) | i1-Q6_K | 28.3 | practically like static Q6_K |
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
## 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 -->
|
mradermacher/HeroBophades-3x7B-GGUF | mradermacher | 2024-05-06T05:10:15Z | 33 | 0 | transformers | [
"transformers",
"gguf",
"en",
"dataset:jondurbin/truthy-dpo-v0.1",
"dataset:kyujinpy/orca_math_dpo",
"dataset:jondurbin/gutenberg-dpo-v0.1",
"base_model:nbeerbower/HeroBophades-3x7B",
"base_model:quantized:nbeerbower/HeroBophades-3x7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T12:06:59Z | ---
base_model: nbeerbower/HeroBophades-3x7B
datasets:
- jondurbin/truthy-dpo-v0.1
- kyujinpy/orca_math_dpo
- jondurbin/gutenberg-dpo-v0.1
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/nbeerbower/HeroBophades-3x7B
<!-- 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/HeroBophades-3x7B-GGUF/resolve/main/HeroBophades-3x7B.Q2_K.gguf) | Q2_K | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/HeroBophades-3x7B-GGUF/resolve/main/HeroBophades-3x7B.IQ3_XS.gguf) | IQ3_XS | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/HeroBophades-3x7B-GGUF/resolve/main/HeroBophades-3x7B.Q3_K_S.gguf) | Q3_K_S | 8.1 | |
| [GGUF](https://huggingface.co/mradermacher/HeroBophades-3x7B-GGUF/resolve/main/HeroBophades-3x7B.IQ3_S.gguf) | IQ3_S | 8.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/HeroBophades-3x7B-GGUF/resolve/main/HeroBophades-3x7B.IQ3_M.gguf) | IQ3_M | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/HeroBophades-3x7B-GGUF/resolve/main/HeroBophades-3x7B.Q3_K_M.gguf) | Q3_K_M | 9.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/HeroBophades-3x7B-GGUF/resolve/main/HeroBophades-3x7B.Q3_K_L.gguf) | Q3_K_L | 9.7 | |
| [GGUF](https://huggingface.co/mradermacher/HeroBophades-3x7B-GGUF/resolve/main/HeroBophades-3x7B.IQ4_XS.gguf) | IQ4_XS | 10.1 | |
| [GGUF](https://huggingface.co/mradermacher/HeroBophades-3x7B-GGUF/resolve/main/HeroBophades-3x7B.Q4_K_S.gguf) | Q4_K_S | 10.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/HeroBophades-3x7B-GGUF/resolve/main/HeroBophades-3x7B.Q4_K_M.gguf) | Q4_K_M | 11.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/HeroBophades-3x7B-GGUF/resolve/main/HeroBophades-3x7B.Q5_K_S.gguf) | Q5_K_S | 12.8 | |
| [GGUF](https://huggingface.co/mradermacher/HeroBophades-3x7B-GGUF/resolve/main/HeroBophades-3x7B.Q5_K_M.gguf) | Q5_K_M | 13.2 | |
| [GGUF](https://huggingface.co/mradermacher/HeroBophades-3x7B-GGUF/resolve/main/HeroBophades-3x7B.Q6_K.gguf) | Q6_K | 15.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/HeroBophades-3x7B-GGUF/resolve/main/HeroBophades-3x7B.Q8_0.gguf) | Q8_0 | 19.8 | 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
## 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 -->
|
mradermacher/NeuralSynthesis-7B-v0.3-GGUF | mradermacher | 2024-05-06T05:10:09Z | 10 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"en",
"base_model:Kukedlc/NeuralSynthesis-7B-v0.3",
"base_model:quantized:Kukedlc/NeuralSynthesis-7B-v0.3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T15:03:53Z | ---
base_model: Kukedlc/NeuralSynthesis-7B-v0.3
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Kukedlc/NeuralSynthesis-7B-v0.3
<!-- 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/NeuralSynthesis-7B-v0.3-GGUF/resolve/main/NeuralSynthesis-7B-v0.3.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralSynthesis-7B-v0.3-GGUF/resolve/main/NeuralSynthesis-7B-v0.3.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralSynthesis-7B-v0.3-GGUF/resolve/main/NeuralSynthesis-7B-v0.3.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralSynthesis-7B-v0.3-GGUF/resolve/main/NeuralSynthesis-7B-v0.3.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/NeuralSynthesis-7B-v0.3-GGUF/resolve/main/NeuralSynthesis-7B-v0.3.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralSynthesis-7B-v0.3-GGUF/resolve/main/NeuralSynthesis-7B-v0.3.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/NeuralSynthesis-7B-v0.3-GGUF/resolve/main/NeuralSynthesis-7B-v0.3.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralSynthesis-7B-v0.3-GGUF/resolve/main/NeuralSynthesis-7B-v0.3.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralSynthesis-7B-v0.3-GGUF/resolve/main/NeuralSynthesis-7B-v0.3.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NeuralSynthesis-7B-v0.3-GGUF/resolve/main/NeuralSynthesis-7B-v0.3.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NeuralSynthesis-7B-v0.3-GGUF/resolve/main/NeuralSynthesis-7B-v0.3.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralSynthesis-7B-v0.3-GGUF/resolve/main/NeuralSynthesis-7B-v0.3.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralSynthesis-7B-v0.3-GGUF/resolve/main/NeuralSynthesis-7B-v0.3.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/NeuralSynthesis-7B-v0.3-GGUF/resolve/main/NeuralSynthesis-7B-v0.3.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/Enterredaas-33b-i1-GGUF | mradermacher | 2024-05-06T05:10:03Z | 5 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Aeala/Enterredaas-33b",
"base_model:quantized:Aeala/Enterredaas-33b",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T16:10:25Z | ---
base_model: Aeala/Enterredaas-33b
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/Aeala/Enterredaas-33b
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Enterredaas-33b-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/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-IQ1_S.gguf) | i1-IQ1_S | 7.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-IQ1_M.gguf) | i1-IQ1_M | 7.8 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 9.7 | |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-IQ2_S.gguf) | i1-IQ2_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-IQ2_M.gguf) | i1-IQ2_M | 11.3 | |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-Q2_K.gguf) | i1-Q2_K | 12.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-IQ3_S.gguf) | i1-IQ3_S | 14.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.2 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-IQ3_M.gguf) | i1-IQ3_M | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 15.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-Q4_0.gguf) | i1-Q4_0 | 18.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 19.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.5 | |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.1 | |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF/resolve/main/Enterredaas-33b.i1-Q6_K.gguf) | i1-Q6_K | 26.8 | practically like static Q6_K |
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
## 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 -->
|
mradermacher/dolphin-mistral-TRACHI-7b-GGUF | mradermacher | 2024-05-06T05:09:58Z | 1 | 0 | transformers | [
"transformers",
"gguf",
"en",
"dataset:norygano/TRACHI",
"base_model:norygano/dolphin-mistral-TRACHI-7b",
"base_model:quantized:norygano/dolphin-mistral-TRACHI-7b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-07T16:46:03Z | ---
base_model: norygano/dolphin-mistral-TRACHI-7b
datasets:
- norygano/TRACHI
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/norygano/dolphin-mistral-TRACHI-7b
<!-- 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/dolphin-mistral-TRACHI-7b-GGUF/resolve/main/dolphin-mistral-TRACHI-7b.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-mistral-TRACHI-7b-GGUF/resolve/main/dolphin-mistral-TRACHI-7b.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-mistral-TRACHI-7b-GGUF/resolve/main/dolphin-mistral-TRACHI-7b.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-mistral-TRACHI-7b-GGUF/resolve/main/dolphin-mistral-TRACHI-7b.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/dolphin-mistral-TRACHI-7b-GGUF/resolve/main/dolphin-mistral-TRACHI-7b.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-mistral-TRACHI-7b-GGUF/resolve/main/dolphin-mistral-TRACHI-7b.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/dolphin-mistral-TRACHI-7b-GGUF/resolve/main/dolphin-mistral-TRACHI-7b.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-mistral-TRACHI-7b-GGUF/resolve/main/dolphin-mistral-TRACHI-7b.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-mistral-TRACHI-7b-GGUF/resolve/main/dolphin-mistral-TRACHI-7b.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/dolphin-mistral-TRACHI-7b-GGUF/resolve/main/dolphin-mistral-TRACHI-7b.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/dolphin-mistral-TRACHI-7b-GGUF/resolve/main/dolphin-mistral-TRACHI-7b.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-mistral-TRACHI-7b-GGUF/resolve/main/dolphin-mistral-TRACHI-7b.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-mistral-TRACHI-7b-GGUF/resolve/main/dolphin-mistral-TRACHI-7b.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/dolphin-mistral-TRACHI-7b-GGUF/resolve/main/dolphin-mistral-TRACHI-7b.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/Tess-72B-v1.5b-i1-GGUF | mradermacher | 2024-05-06T05:09:52Z | 4 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:migtissera/Tess-72B-v1.5b",
"base_model:quantized:migtissera/Tess-72B-v1.5b",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T17:30:16Z | ---
base_model: migtissera/Tess-72B-v1.5b
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/Qwen/Qwen-72B/blob/main/LICENSE
license_name: qwen-72b-licence
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/migtissera/Tess-72B-v1.5b
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Tess-72B-v1.5b-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/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-IQ1_S.gguf) | i1-IQ1_S | 16.3 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-IQ1_M.gguf) | i1-IQ1_M | 17.7 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 19.9 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 21.9 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-IQ2_S.gguf) | i1-IQ2_S | 23.5 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-IQ2_M.gguf) | i1-IQ2_M | 25.3 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-Q2_K.gguf) | i1-Q2_K | 27.2 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 27.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 30.0 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-IQ3_S.gguf) | i1-IQ3_S | 31.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 31.7 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-IQ3_M.gguf) | i1-IQ3_M | 33.4 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 35.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 38.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 38.9 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-Q4_0.gguf) | i1-Q4_0 | 41.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 41.4 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 43.9 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-Q5_K_M.gguf.part2of2) | i1-Q5_K_M | 51.4 | |
| [PART 1](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF/resolve/main/Tess-72B-v1.5b.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 59.4 | practically like static Q6_K |
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
## 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 -->
|
mradermacher/Pearl-3x7B-GGUF | mradermacher | 2024-05-06T05:09:47Z | 75 | 1 | transformers | [
"transformers",
"gguf",
"moe",
"frankenmoe",
"merge",
"mergekit",
"lazymergekit",
"dvilasuero/DistilabelBeagle14-7B",
"beowolx/CodeNinja-1.0-OpenChat-7B",
"WizardLM/WizardMath-7B-V1.1",
"Maths",
"Code",
"Python",
"en",
"base_model:louisbrulenaudet/Pearl-3x7B",
"base_model:quantized:louisbrulenaudet/Pearl-3x7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-07T18:52:17Z | ---
base_model: louisbrulenaudet/Pearl-3x7B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- dvilasuero/DistilabelBeagle14-7B
- beowolx/CodeNinja-1.0-OpenChat-7B
- WizardLM/WizardMath-7B-V1.1
- Maths
- Code
- Python
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/louisbrulenaudet/Pearl-3x7B
<!-- 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/Pearl-3x7B-GGUF/resolve/main/Pearl-3x7B.Q2_K.gguf) | Q2_K | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/Pearl-3x7B-GGUF/resolve/main/Pearl-3x7B.IQ3_XS.gguf) | IQ3_XS | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/Pearl-3x7B-GGUF/resolve/main/Pearl-3x7B.Q3_K_S.gguf) | Q3_K_S | 8.1 | |
| [GGUF](https://huggingface.co/mradermacher/Pearl-3x7B-GGUF/resolve/main/Pearl-3x7B.IQ3_S.gguf) | IQ3_S | 8.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Pearl-3x7B-GGUF/resolve/main/Pearl-3x7B.IQ3_M.gguf) | IQ3_M | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/Pearl-3x7B-GGUF/resolve/main/Pearl-3x7B.Q3_K_M.gguf) | Q3_K_M | 9.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Pearl-3x7B-GGUF/resolve/main/Pearl-3x7B.Q3_K_L.gguf) | Q3_K_L | 9.7 | |
| [GGUF](https://huggingface.co/mradermacher/Pearl-3x7B-GGUF/resolve/main/Pearl-3x7B.IQ4_XS.gguf) | IQ4_XS | 10.1 | |
| [GGUF](https://huggingface.co/mradermacher/Pearl-3x7B-GGUF/resolve/main/Pearl-3x7B.Q4_K_S.gguf) | Q4_K_S | 10.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Pearl-3x7B-GGUF/resolve/main/Pearl-3x7B.Q4_K_M.gguf) | Q4_K_M | 11.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Pearl-3x7B-GGUF/resolve/main/Pearl-3x7B.Q5_K_S.gguf) | Q5_K_S | 12.8 | |
| [GGUF](https://huggingface.co/mradermacher/Pearl-3x7B-GGUF/resolve/main/Pearl-3x7B.Q5_K_M.gguf) | Q5_K_M | 13.2 | |
| [GGUF](https://huggingface.co/mradermacher/Pearl-3x7B-GGUF/resolve/main/Pearl-3x7B.Q6_K.gguf) | Q6_K | 15.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Pearl-3x7B-GGUF/resolve/main/Pearl-3x7B.Q8_0.gguf) | Q8_0 | 19.8 | 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
## 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 -->
|
mradermacher/Fuse-Dolphin-7B-GGUF | mradermacher | 2024-05-06T05:09:41Z | 3 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"en",
"base_model:bunnycore/Fuse-Dolphin-7B",
"base_model:quantized:bunnycore/Fuse-Dolphin-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-07T21:15:42Z | ---
base_model: bunnycore/Fuse-Dolphin-7B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/bunnycore/Fuse-Dolphin-7B
<!-- 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/Fuse-Dolphin-7B-GGUF/resolve/main/Fuse-Dolphin-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Fuse-Dolphin-7B-GGUF/resolve/main/Fuse-Dolphin-7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Fuse-Dolphin-7B-GGUF/resolve/main/Fuse-Dolphin-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Fuse-Dolphin-7B-GGUF/resolve/main/Fuse-Dolphin-7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Fuse-Dolphin-7B-GGUF/resolve/main/Fuse-Dolphin-7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Fuse-Dolphin-7B-GGUF/resolve/main/Fuse-Dolphin-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Fuse-Dolphin-7B-GGUF/resolve/main/Fuse-Dolphin-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Fuse-Dolphin-7B-GGUF/resolve/main/Fuse-Dolphin-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Fuse-Dolphin-7B-GGUF/resolve/main/Fuse-Dolphin-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Fuse-Dolphin-7B-GGUF/resolve/main/Fuse-Dolphin-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Fuse-Dolphin-7B-GGUF/resolve/main/Fuse-Dolphin-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Fuse-Dolphin-7B-GGUF/resolve/main/Fuse-Dolphin-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Fuse-Dolphin-7B-GGUF/resolve/main/Fuse-Dolphin-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Fuse-Dolphin-7B-GGUF/resolve/main/Fuse-Dolphin-7B.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF | mradermacher | 2024-05-06T05:09:38Z | 50 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:ChuckMcSneed/WinterGoddess-1.4x-70b-32k",
"base_model:quantized:ChuckMcSneed/WinterGoddess-1.4x-70b-32k",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T21:20:06Z | ---
base_model: ChuckMcSneed/WinterGoddess-1.4x-70b-32k
language:
- en
library_name: transformers
license: llama2
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/ChuckMcSneed/WinterGoddess-1.4x-70b-32k
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-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/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-IQ1_S.gguf) | i1-IQ1_S | 14.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-IQ1_M.gguf) | i1-IQ1_M | 16.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 18.4 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-IQ2_XS.gguf) | i1-IQ2_XS | 20.4 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-IQ2_S.gguf) | i1-IQ2_S | 21.5 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-IQ2_M.gguf) | i1-IQ2_M | 23.3 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-Q2_K.gguf) | i1-Q2_K | 25.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 26.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-IQ3_XS.gguf) | i1-IQ3_XS | 28.4 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-IQ3_S.gguf) | i1-IQ3_S | 30.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-Q3_K_S.gguf) | i1-Q3_K_S | 30.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-IQ3_M.gguf) | i1-IQ3_M | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-Q3_K_M.gguf) | i1-Q3_K_M | 33.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-Q3_K_L.gguf) | i1-Q3_K_L | 36.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-IQ4_XS.gguf) | i1-IQ4_XS | 36.9 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-Q4_0.gguf) | i1-Q4_0 | 39.1 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-Q4_K_S.gguf) | i1-Q4_K_S | 39.3 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-Q4_K_M.gguf) | i1-Q4_K_M | 41.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-Q5_K_S.gguf) | i1-Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-Q5_K_M.gguf) | i1-Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 56.7 | practically like static Q6_K |
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
## 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 -->
|
mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF | mradermacher | 2024-05-06T05:09:35Z | 221 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Aeala/GPT4-x-AlpacaDente2-30b",
"base_model:quantized:Aeala/GPT4-x-AlpacaDente2-30b",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T21:25:07Z | ---
base_model: Aeala/GPT4-x-AlpacaDente2-30b
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/Aeala/GPT4-x-AlpacaDente2-30b
**This uses only 40k tokens of my standard set, as the model overflowed with more.**
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-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/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-IQ1_S.gguf) | i1-IQ1_S | 7.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-IQ1_M.gguf) | i1-IQ1_M | 7.8 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 9.7 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-IQ2_S.gguf) | i1-IQ2_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-IQ2_M.gguf) | i1-IQ2_M | 11.3 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-Q2_K.gguf) | i1-Q2_K | 12.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-IQ3_S.gguf) | i1-IQ3_S | 14.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.2 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-IQ3_M.gguf) | i1-IQ3_M | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 15.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-Q4_0.gguf) | i1-Q4_0 | 18.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 19.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.5 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.1 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.i1-Q6_K.gguf) | i1-Q6_K | 26.8 | practically like static Q6_K |
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
## 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 -->
|
mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF | mradermacher | 2024-05-06T05:09:20Z | 17 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"merge",
"en",
"base_model:brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity",
"base_model:quantized:brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-08T00:38:32Z | ---
base_model: brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
license_name: yi-license
quantized_by: mradermacher
tags:
- text-generation-inference
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-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/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-IQ1_S.gguf) | i1-IQ1_S | 7.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-IQ1_M.gguf) | i1-IQ1_M | 8.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-IQ2_S.gguf) | i1-IQ2_S | 11.0 | |
| [GGUF](https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-IQ2_M.gguf) | i1-IQ2_M | 11.9 | |
| [GGUF](https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-Q2_K.gguf) | i1-Q2_K | 12.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 13.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-IQ3_XS.gguf) | i1-IQ3_XS | 14.3 | |
| [GGUF](https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-Q3_K_S.gguf) | i1-Q3_K_S | 15.1 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-IQ3_S.gguf) | i1-IQ3_S | 15.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-IQ3_M.gguf) | i1-IQ3_M | 15.7 | |
| [GGUF](https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-Q3_K_L.gguf) | i1-Q3_K_L | 18.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-IQ4_XS.gguf) | i1-IQ4_XS | 18.6 | |
| [GGUF](https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-Q4_0.gguf) | i1-Q4_0 | 19.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-Q4_K_S.gguf) | i1-Q4_K_S | 19.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-Q5_K_S.gguf) | i1-Q5_K_S | 23.8 | |
| [GGUF](https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-Q5_K_M.gguf) | i1-Q5_K_M | 24.4 | |
| [GGUF](https://huggingface.co/mradermacher/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity-i1-GGUF/resolve/main/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity.i1-Q6_K.gguf) | i1-Q6_K | 28.3 | practically like static Q6_K |
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
## 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 -->
|
mradermacher/Honyaku-7b-v2-GGUF | mradermacher | 2024-05-06T05:09:17Z | 7 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:aixsatoshi/Honyaku-7b-v2",
"base_model:quantized:aixsatoshi/Honyaku-7b-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-08T02:22:57Z | ---
base_model: aixsatoshi/Honyaku-7b-v2
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/aixsatoshi/Honyaku-7b-v2
<!-- 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/Honyaku-7b-v2-GGUF/resolve/main/Honyaku-7b-v2.Q2_K.gguf) | Q2_K | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Honyaku-7b-v2-GGUF/resolve/main/Honyaku-7b-v2.IQ3_XS.gguf) | IQ3_XS | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/Honyaku-7b-v2-GGUF/resolve/main/Honyaku-7b-v2.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Honyaku-7b-v2-GGUF/resolve/main/Honyaku-7b-v2.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Honyaku-7b-v2-GGUF/resolve/main/Honyaku-7b-v2.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Honyaku-7b-v2-GGUF/resolve/main/Honyaku-7b-v2.Q3_K_M.gguf) | Q3_K_M | 3.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Honyaku-7b-v2-GGUF/resolve/main/Honyaku-7b-v2.Q3_K_L.gguf) | Q3_K_L | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Honyaku-7b-v2-GGUF/resolve/main/Honyaku-7b-v2.IQ4_XS.gguf) | IQ4_XS | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Honyaku-7b-v2-GGUF/resolve/main/Honyaku-7b-v2.Q4_K_S.gguf) | Q4_K_S | 4.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Honyaku-7b-v2-GGUF/resolve/main/Honyaku-7b-v2.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Honyaku-7b-v2-GGUF/resolve/main/Honyaku-7b-v2.Q5_K_S.gguf) | Q5_K_S | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Honyaku-7b-v2-GGUF/resolve/main/Honyaku-7b-v2.Q5_K_M.gguf) | Q5_K_M | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Honyaku-7b-v2-GGUF/resolve/main/Honyaku-7b-v2.Q6_K.gguf) | Q6_K | 6.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Honyaku-7b-v2-GGUF/resolve/main/Honyaku-7b-v2.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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 -->
|
mradermacher/GPT4-x-AlpacaDente-30b-GGUF | mradermacher | 2024-05-06T05:09:15Z | 9 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Aeala/GPT4-x-AlpacaDente-30b",
"base_model:quantized:Aeala/GPT4-x-AlpacaDente-30b",
"endpoints_compatible",
"region:us"
] | null | 2024-04-08T03:27:38Z | ---
base_model: Aeala/GPT4-x-AlpacaDente-30b
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Aeala/GPT4-x-AlpacaDente-30b
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/GPT4-x-AlpacaDente-30b-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/GPT4-x-AlpacaDente-30b-GGUF/resolve/main/GPT4-x-AlpacaDente-30b.Q2_K.gguf) | Q2_K | 12.1 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente-30b-GGUF/resolve/main/GPT4-x-AlpacaDente-30b.IQ3_XS.gguf) | IQ3_XS | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente-30b-GGUF/resolve/main/GPT4-x-AlpacaDente-30b.IQ3_S.gguf) | IQ3_S | 14.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente-30b-GGUF/resolve/main/GPT4-x-AlpacaDente-30b.Q3_K_S.gguf) | Q3_K_S | 14.2 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente-30b-GGUF/resolve/main/GPT4-x-AlpacaDente-30b.IQ3_M.gguf) | IQ3_M | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente-30b-GGUF/resolve/main/GPT4-x-AlpacaDente-30b.Q3_K_M.gguf) | Q3_K_M | 15.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente-30b-GGUF/resolve/main/GPT4-x-AlpacaDente-30b.Q3_K_L.gguf) | Q3_K_L | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente-30b-GGUF/resolve/main/GPT4-x-AlpacaDente-30b.IQ4_XS.gguf) | IQ4_XS | 17.6 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente-30b-GGUF/resolve/main/GPT4-x-AlpacaDente-30b.Q4_K_S.gguf) | Q4_K_S | 18.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente-30b-GGUF/resolve/main/GPT4-x-AlpacaDente-30b.Q4_K_M.gguf) | Q4_K_M | 19.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente-30b-GGUF/resolve/main/GPT4-x-AlpacaDente-30b.Q5_K_S.gguf) | Q5_K_S | 22.5 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente-30b-GGUF/resolve/main/GPT4-x-AlpacaDente-30b.Q5_K_M.gguf) | Q5_K_M | 23.1 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente-30b-GGUF/resolve/main/GPT4-x-AlpacaDente-30b.Q6_K.gguf) | Q6_K | 26.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente-30b-GGUF/resolve/main/GPT4-x-AlpacaDente-30b.Q8_0.gguf) | Q8_0 | 34.7 | 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
## 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 -->
|
mradermacher/Chimera-Apex-7B-GGUF | mradermacher | 2024-05-06T05:09:05Z | 295 | 2 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"en",
"base_model:bunnycore/Chimera-Apex-7B",
"base_model:quantized:bunnycore/Chimera-Apex-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-08T03:57:50Z | ---
base_model: bunnycore/Chimera-Apex-7B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/bunnycore/Chimera-Apex-7B
<!-- 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/Chimera-Apex-7B-GGUF/resolve/main/Chimera-Apex-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Chimera-Apex-7B-GGUF/resolve/main/Chimera-Apex-7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Chimera-Apex-7B-GGUF/resolve/main/Chimera-Apex-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Chimera-Apex-7B-GGUF/resolve/main/Chimera-Apex-7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Chimera-Apex-7B-GGUF/resolve/main/Chimera-Apex-7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Chimera-Apex-7B-GGUF/resolve/main/Chimera-Apex-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Chimera-Apex-7B-GGUF/resolve/main/Chimera-Apex-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Chimera-Apex-7B-GGUF/resolve/main/Chimera-Apex-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Chimera-Apex-7B-GGUF/resolve/main/Chimera-Apex-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Chimera-Apex-7B-GGUF/resolve/main/Chimera-Apex-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Chimera-Apex-7B-GGUF/resolve/main/Chimera-Apex-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Chimera-Apex-7B-GGUF/resolve/main/Chimera-Apex-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Chimera-Apex-7B-GGUF/resolve/main/Chimera-Apex-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Chimera-Apex-7B-GGUF/resolve/main/Chimera-Apex-7B.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/BiscuitRP-8x7B-GGUF | mradermacher | 2024-05-06T05:09:02Z | 33 | 1 | transformers | [
"transformers",
"gguf",
"rp",
"roleplay",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-08T04:04:27Z | ---
base_model: Fredithefish/BiscuitRP-8x7B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- rp
- roleplay
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Fredithefish/BiscuitRP-8x7B
<!-- 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/BiscuitRP-8x7B-GGUF/resolve/main/BiscuitRP-8x7B.Q2_K.gguf) | Q2_K | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/BiscuitRP-8x7B-GGUF/resolve/main/BiscuitRP-8x7B.IQ3_XS.gguf) | IQ3_XS | 19.4 | |
| [GGUF](https://huggingface.co/mradermacher/BiscuitRP-8x7B-GGUF/resolve/main/BiscuitRP-8x7B.IQ3_S.gguf) | IQ3_S | 20.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/BiscuitRP-8x7B-GGUF/resolve/main/BiscuitRP-8x7B.Q3_K_S.gguf) | Q3_K_S | 20.5 | |
| [GGUF](https://huggingface.co/mradermacher/BiscuitRP-8x7B-GGUF/resolve/main/BiscuitRP-8x7B.IQ3_M.gguf) | IQ3_M | 21.5 | |
| [GGUF](https://huggingface.co/mradermacher/BiscuitRP-8x7B-GGUF/resolve/main/BiscuitRP-8x7B.Q3_K_M.gguf) | Q3_K_M | 22.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/BiscuitRP-8x7B-GGUF/resolve/main/BiscuitRP-8x7B.Q3_K_L.gguf) | Q3_K_L | 24.3 | |
| [GGUF](https://huggingface.co/mradermacher/BiscuitRP-8x7B-GGUF/resolve/main/BiscuitRP-8x7B.IQ4_XS.gguf) | IQ4_XS | 25.5 | |
| [GGUF](https://huggingface.co/mradermacher/BiscuitRP-8x7B-GGUF/resolve/main/BiscuitRP-8x7B.Q4_K_S.gguf) | Q4_K_S | 26.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/BiscuitRP-8x7B-GGUF/resolve/main/BiscuitRP-8x7B.Q4_K_M.gguf) | Q4_K_M | 28.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/BiscuitRP-8x7B-GGUF/resolve/main/BiscuitRP-8x7B.Q5_K_S.gguf) | Q5_K_S | 32.3 | |
| [GGUF](https://huggingface.co/mradermacher/BiscuitRP-8x7B-GGUF/resolve/main/BiscuitRP-8x7B.Q5_K_M.gguf) | Q5_K_M | 33.3 | |
| [GGUF](https://huggingface.co/mradermacher/BiscuitRP-8x7B-GGUF/resolve/main/BiscuitRP-8x7B.Q6_K.gguf) | Q6_K | 38.5 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/BiscuitRP-8x7B-GGUF/resolve/main/BiscuitRP-8x7B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BiscuitRP-8x7B-GGUF/resolve/main/BiscuitRP-8x7B.Q8_0.gguf.part2of2) | Q8_0 | 49.7 | 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
## 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 -->
|
mradermacher/CelestiaRP-8x7B-i1-GGUF | mradermacher | 2024-05-06T05:08:58Z | 2 | 1 | transformers | [
"transformers",
"gguf",
"en",
"endpoints_compatible",
"region:us"
] | null | 2024-04-08T05:20:14Z | ---
base_model: Fredithefish/CelestiaRP-8x7B
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/Fredithefish/CelestiaRP-8x7B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/CelestiaRP-8x7B-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/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-IQ1_S.gguf) | i1-IQ1_S | 9.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-IQ1_M.gguf) | i1-IQ1_M | 10.8 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 12.5 | |
| [GGUF](https://huggingface.co/mradermacher/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 14.0 | |
| [GGUF](https://huggingface.co/mradermacher/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-IQ2_S.gguf) | i1-IQ2_S | 14.0 | |
| [GGUF](https://huggingface.co/mradermacher/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-IQ2_M.gguf) | i1-IQ2_M | 15.4 | |
| [GGUF](https://huggingface.co/mradermacher/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-Q2_K.gguf) | i1-Q2_K | 17.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 18.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 19.5 | |
| [GGUF](https://huggingface.co/mradermacher/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-IQ3_S.gguf) | i1-IQ3_S | 20.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 20.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-IQ3_M.gguf) | i1-IQ3_M | 21.5 | |
| [GGUF](https://huggingface.co/mradermacher/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 22.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 24.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 25.2 | |
| [GGUF](https://huggingface.co/mradermacher/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-Q4_0.gguf) | i1-Q4_0 | 26.7 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 26.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 28.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 32.3 | |
| [GGUF](https://huggingface.co/mradermacher/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 33.3 | |
| [GGUF](https://huggingface.co/mradermacher/CelestiaRP-8x7B-i1-GGUF/resolve/main/CelestiaRP-8x7B.i1-Q6_K.gguf) | i1-Q6_K | 38.5 | practically like static Q6_K |
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
## 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 -->
|
mradermacher/zephyr-7b-alpha-GGUF | mradermacher | 2024-05-06T05:08:56Z | 59 | 0 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"en",
"dataset:stingning/ultrachat",
"dataset:openbmb/UltraFeedback",
"base_model:HuggingFaceH4/zephyr-7b-alpha",
"base_model:quantized:HuggingFaceH4/zephyr-7b-alpha",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-08T05:46:11Z | ---
base_model: HuggingFaceH4/zephyr-7b-alpha
datasets:
- stingning/ultrachat
- openbmb/UltraFeedback
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha
<!-- 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/zephyr-7b-alpha-GGUF/resolve/main/zephyr-7b-alpha.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-alpha-GGUF/resolve/main/zephyr-7b-alpha.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-alpha-GGUF/resolve/main/zephyr-7b-alpha.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-alpha-GGUF/resolve/main/zephyr-7b-alpha.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-alpha-GGUF/resolve/main/zephyr-7b-alpha.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-alpha-GGUF/resolve/main/zephyr-7b-alpha.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-alpha-GGUF/resolve/main/zephyr-7b-alpha.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-alpha-GGUF/resolve/main/zephyr-7b-alpha.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-alpha-GGUF/resolve/main/zephyr-7b-alpha.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-alpha-GGUF/resolve/main/zephyr-7b-alpha.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-alpha-GGUF/resolve/main/zephyr-7b-alpha.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-alpha-GGUF/resolve/main/zephyr-7b-alpha.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-alpha-GGUF/resolve/main/zephyr-7b-alpha.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-alpha-GGUF/resolve/main/zephyr-7b-alpha.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/zephyr-7b-beta-GGUF | mradermacher | 2024-05-06T05:08:45Z | 89 | 0 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"en",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_model:quantized:HuggingFaceH4/zephyr-7b-beta",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-08T07:40:20Z | ---
base_model: HuggingFaceH4/zephyr-7b-beta
datasets:
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/ultrafeedback_binarized
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/HuggingFaceH4/zephyr-7b-beta
<!-- 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/zephyr-7b-beta-GGUF/resolve/main/zephyr-7b-beta.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-GGUF/resolve/main/zephyr-7b-beta.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-GGUF/resolve/main/zephyr-7b-beta.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-GGUF/resolve/main/zephyr-7b-beta.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-GGUF/resolve/main/zephyr-7b-beta.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-GGUF/resolve/main/zephyr-7b-beta.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-GGUF/resolve/main/zephyr-7b-beta.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-GGUF/resolve/main/zephyr-7b-beta.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-GGUF/resolve/main/zephyr-7b-beta.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-GGUF/resolve/main/zephyr-7b-beta.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-GGUF/resolve/main/zephyr-7b-beta.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-GGUF/resolve/main/zephyr-7b-beta.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-GGUF/resolve/main/zephyr-7b-beta.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-GGUF/resolve/main/zephyr-7b-beta.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
mradermacher/MadMix-v0.2-GGUF | mradermacher | 2024-05-06T05:08:30Z | 74 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"merge",
"openchat",
"7b",
"zephyr",
"en",
"base_model:Fredithefish/MadMix-v0.2",
"base_model:quantized:Fredithefish/MadMix-v0.2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-08T09:36:49Z | ---
base_model: Fredithefish/MadMix-v0.2
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mistral
- merge
- openchat
- 7b
- zephyr
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Fredithefish/MadMix-v0.2
<!-- 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/MadMix-v0.2-GGUF/resolve/main/MadMix-v0.2.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/MadMix-v0.2-GGUF/resolve/main/MadMix-v0.2.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/MadMix-v0.2-GGUF/resolve/main/MadMix-v0.2.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/MadMix-v0.2-GGUF/resolve/main/MadMix-v0.2.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/MadMix-v0.2-GGUF/resolve/main/MadMix-v0.2.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/MadMix-v0.2-GGUF/resolve/main/MadMix-v0.2.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MadMix-v0.2-GGUF/resolve/main/MadMix-v0.2.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/MadMix-v0.2-GGUF/resolve/main/MadMix-v0.2.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/MadMix-v0.2-GGUF/resolve/main/MadMix-v0.2.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MadMix-v0.2-GGUF/resolve/main/MadMix-v0.2.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MadMix-v0.2-GGUF/resolve/main/MadMix-v0.2.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/MadMix-v0.2-GGUF/resolve/main/MadMix-v0.2.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/MadMix-v0.2-GGUF/resolve/main/MadMix-v0.2.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/MadMix-v0.2-GGUF/resolve/main/MadMix-v0.2.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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 -->
|
Subsets and Splits