---
base_model: 01-ai/Yi-34B
datasets:
- teknium/OpenHermes-2.5
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- yi
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
---
## About

weighted/imatrix quants of https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B

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## 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/Nous-Hermes-2-Yi-34B-i1-GGUF/resolve/main/Nous-Hermes-2-Yi-34B.i1-IQ2_M.gguf) | i1-IQ2_M | 12.5 |  |
| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-2-Yi-34B-i1-GGUF/resolve/main/Nous-Hermes-2-Yi-34B.i1-Q2_K.gguf) | i1-Q2_K | 13.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-2-Yi-34B-i1-GGUF/resolve/main/Nous-Hermes-2-Yi-34B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 14.0 | fast, lower quality |
| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-2-Yi-34B-i1-GGUF/resolve/main/Nous-Hermes-2-Yi-34B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 14.8 |  |
| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-2-Yi-34B-i1-GGUF/resolve/main/Nous-Hermes-2-Yi-34B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 15.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-2-Yi-34B-i1-GGUF/resolve/main/Nous-Hermes-2-Yi-34B.i1-IQ3_S.gguf) | i1-IQ3_S | 15.7 | fast, beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-2-Yi-34B-i1-GGUF/resolve/main/Nous-Hermes-2-Yi-34B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 17.3 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-2-Yi-34B-i1-GGUF/resolve/main/Nous-Hermes-2-Yi-34B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 18.8 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-2-Yi-34B-i1-GGUF/resolve/main/Nous-Hermes-2-Yi-34B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 20.2 | almost as good as Q4_K_M |
| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-2-Yi-34B-i1-GGUF/resolve/main/Nous-Hermes-2-Yi-34B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 21.3 | fast, medium quality |
| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-2-Yi-34B-i1-GGUF/resolve/main/Nous-Hermes-2-Yi-34B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 24.3 |  |
| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-2-Yi-34B-i1-GGUF/resolve/main/Nous-Hermes-2-Yi-34B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 25.0 | best weighted quant |


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

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