File size: 2,663 Bytes
2b543fb c701d83 3f4b8be 2b543fb 55b56f5 c701d83 4c7deff c701d83 472adf6 c701d83 1878de7 c701d83 d57afc9 5059e19 45ae8a7 c701d83 7efa174 c701d83 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
---
license: other
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- merge
---
# This is not a great model, succeeded by a new merge: **https://huggingface.co/brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties**
**NousResearch/Nous-Capybara-34B**, **migtissera/Tess-M-v1.2** and **migtissera/Tess-M-v1.3** merged with a new, experimental implementation of "dare ties" via mergekit. See:
> Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
https://github.com/yule-BUAA/MergeLM
https://github.com/cg123/mergekit/tree/dare-tokenizer
Highly experimental and still being tested! But this should yield a better merge than a typical linear/slerp merge or even a ties merge.
***
Merged with the following config, and the tokenizer from Yi Llamafied:
```
models:
- model: /home/alpha/Storage/Models/Raw/larryvrh_Yi-34B-200K-Llamafied
# no parameters necessary for base model
- model: /home/alpha/Storage/Models/Raw/migtissera_Tess-M-v1.3
parameters:
weight: 0.50
density: 0.56
- model: /home/alpha/Storage/Models/Raw/migtissera_Tess-M-v1.2
parameters:
weight: 0.20
density: 0.50
- model: /home/alpha/Storage/Models/Raw/Nous-Capybara-34B
parameters:
weight: 0.50
density: 0.56
merge_method: dare_ties
base_model: /home/alpha/Storage/Models/Raw/larryvrh_Yi-34B-200K-Llamafied
parameters:
int8_mask: true
dtype: bfloat16
```
Tess 1.2 (at a low weight) and 1.3 were used because, according to the trainer, they were trained on different datasets: https://migel.substack.com/p/learnings-from-training-tess
As the Tess creator warned about, if the model repeats at high context like Tess 1.2, let me know!
I chose not to include other finetunes, such as Dolphin, because they aren't trained on the 200K base. If any other 200K finetunes pop up, let me know.
***
## Prompt template: Orca-Vicuna
```
SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
```
Being a Yi model, try disabling the BOS token and/or running a lower temperature with MinP if output doesn't seem right.
Sometimes the model "spells out" the stop token as `</s>` like Capybara, so you may need to add `</s>` as an additional stopping condition.
***
Credits:
https://github.com/cg123/mergekit/tree/dare-tokenizer
https://huggingface.co/NousResearch/Nous-Capybara-34B/
https://huggingface.co/migtissera/Tess-M-v1.2
https://huggingface.co/migtissera/Tess-M-v1.3
https://huggingface.co/larryvrh/Yi-34B-200K-Llamafied
https://huggingface.co/01-ai/Yi-34B-200K |