auto-patch README.md

README.md

@@ -1,5 +1,52 @@
+---
+datasets:
+- wenbopan/Fusang-v1
+- wenbopan/OpenOrca-zh-20k
+exported_from: wenbopan/Faro-Yi-34B-200K
+language:
+- en
+library_name: transformers
+license: mit
+quantized_by: mradermacher
+---
+## About
+
 <!-- ### convert_type:  -->
 <!-- ### vocab_type:  -->
 weighted/imatrix quants of https://huggingface.co/wenbopan/Faro-Yi-34B-200K
 
 **This uses my "quarter" training set of 40k tokens as the model overflowed after 25k tokens with the standard set.**
+
+
+<!-- provided-files -->
+static quants are available at https://huggingface.co/mradermacher/Faro-Yi-34B-200K-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/Faro-Yi-34B-200K-i1-GGUF/resolve/main/Faro-Yi-34B-200K.i1-Q2_K.gguf) | i1-Q2_K | 13.5 | IQ3_XXS probably better |
+
+
+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
+
+## 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 -->