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---
base_model: vicgalle/gpt2-open-instruct-v1
datasets:
- hakurei/open-instruct-v1
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
tags:
- instruction-tuning
- gpt2
- alpaca
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/vicgalle/gpt2-open-instruct-v1
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/gpt2-open-instruct-v1-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/gpt2-open-instruct-v1-GGUF/resolve/main/gpt2-open-instruct-v1.Q2_K.gguf) | Q2_K | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-open-instruct-v1-GGUF/resolve/main/gpt2-open-instruct-v1.Q3_K_S.gguf) | Q3_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-open-instruct-v1-GGUF/resolve/main/gpt2-open-instruct-v1.Q3_K_M.gguf) | Q3_K_M | 0.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gpt2-open-instruct-v1-GGUF/resolve/main/gpt2-open-instruct-v1.Q3_K_L.gguf) | Q3_K_L | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-open-instruct-v1-GGUF/resolve/main/gpt2-open-instruct-v1.IQ4_XS.gguf) | IQ4_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-open-instruct-v1-GGUF/resolve/main/gpt2-open-instruct-v1.Q4_K_S.gguf) | Q4_K_S | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gpt2-open-instruct-v1-GGUF/resolve/main/gpt2-open-instruct-v1.Q4_K_M.gguf) | Q4_K_M | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gpt2-open-instruct-v1-GGUF/resolve/main/gpt2-open-instruct-v1.Q5_K_S.gguf) | Q5_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-open-instruct-v1-GGUF/resolve/main/gpt2-open-instruct-v1.Q5_K_M.gguf) | Q5_K_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-open-instruct-v1-GGUF/resolve/main/gpt2-open-instruct-v1.Q6_K.gguf) | Q6_K | 0.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/gpt2-open-instruct-v1-GGUF/resolve/main/gpt2-open-instruct-v1.Q8_0.gguf) | Q8_0 | 0.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/gpt2-open-instruct-v1-GGUF/resolve/main/gpt2-open-instruct-v1.f16.gguf) | f16 | 0.4 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->

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