---
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:
- text-generation-inference
- merge
---
[**Nous-Capybara-34B**](https://huggingface.co/NousResearch/Nous-Capybara-34B/), [**Tess-M-v1.4**](https://huggingface.co/migtissera/Tess-34B-v1.4), [**Airoboros-3_1-yi-34b-200k**](https://huggingface.co/bhenrym14/airoboros-3_1-yi-34b-200k), [**PlatYi-34B-200K-Q**](https://huggingface.co/kyujinpy/PlatYi-34B-200k-Q-FastChat), [**Pallas-0.4**](https://huggingface.co/Mihaiii/Pallas-0.4), [**Yi-34B-200K-AEZAKMI-v2**](https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-v2), and a tiny bit of [**SUS-Chat-34B**](https://huggingface.co/SUSTech/SUS-Chat-34B) merged with a new, experimental implementation of "dare ties" via mergekit.

See the main model card: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-merge-v5

The merge was then quantized with exllamav2's 0.0.11 brand new exl2 quantization, using 300K tokens from a sci fi story, a fantasy story, and a Vicuna format chat as profiling data, at a high context size. This should results in excellent writing performance for the model size.

This 4bpw quantization can fit ~**45K Context on a 24GB GPU** at high quality.
***
## Prompt template: Orca-Vicuna
```
SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
```
It might recognize ChatML, or maybe Llama-chat from Airoboros.

Sometimes the model "spells out" the stop token as `</s>` like Capybara, so you may need to add `</s>` as an additional stopping condition.
***
## Running
Being a Yi model, try running a lower temperature with 0.05-0.1 MinP, a little repitition penalty, and no other samplers. Yi tends to run "hot" by default.

24GB GPUs can run Yi-34B-200K models at **45K-75K context** with exllamav2, and performant UIs like [exui](https://github.com/turboderp/exui). I go into more detail in this [post](https://old.reddit.com/r/LocalLLaMA/comments/1896igc/how_i_run_34b_models_at_75k_context_on_24gb_fast/)

***
## Commands
First pass: 
```
python convert.py --in_dir /home/alpha/FastModels/Yi-34B-200K-DARE-merge-v5 -o /home/alpha/FastModels/scratch -om /home/alpha/FastModels/v5.json --cal_dataset /home/alpha/Documents/smol.parquet -ml 32768 -mr 9 -ss 4096 -b 4.0 -hb 6 -nr
```
Second pass: 
```
python convert.py --in_dir /home/alpha/FastModels/Yi-34B-200K-DARE-merge-v5 -o /home/alpha/FastModels/scratch -m /home/alpha/FastModels/v5.json --cal_dataset /home/alpha/Documents/medium.parquet -l 12288 -r 29 -ml 32768 -mr 9  -ss 4096 -b 4.0 -hb 6 -cf /home/alpha/FastModels/Yi-34B-200K-DARE-merge-v5-exl2-4bpw-fiction -nr
```

Merged in mergekit with the following config, and the tokenizer from chargoddard's Yi-Llama:

```
models:
  - model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama
    # No parameters necessary for base model
  - model: /home/alpha/Storage/Models/Raw/migtissera_Tess-34B-v1.4
    # Less weight than previous merge since Pallas is a finetune of Tess
    parameters:
      weight: 0.14
      density: 0.62
  - model: /home/alpha/FastModels/Mihaiii_Pallas-0.4
    parameters:
      weight: 0.14
      density: 0.62
  - model: /home/alpha//Storage/Models/Raw/bhenrym14_airoboros-3_1-yi-34b-200k
    parameters:
      weight: 0.14
      density: 0.52
  - model: /home/alpha/Storage/Models/Raw/Nous-Capybara-34B
    parameters:
      weight: 0.22
      density: 0.62
  - model: /home/alpha/Storage/Models/Raw/kyujinpy_PlatYi-34B-200k-Q-FastChat
    parameters:
      weight: 0.14
      density: 0.52
  #- model: /home/alpha/Storage/Models/Raw/ehartford_dolphin-2.2-yi-34b-200k
  #  Dolphin 200K seems to be broken according to multiple leaderboards and perplexity tests?
  #  parameters:
  #    weight: 0.15
  #    density: 0.6
  - model: /home/alpha/Models/Raw/adamo1139_Yi-34B-200K-AEZAKMI-v2
    parameters:
      weight: 0.14
      density: 0.52
  - model: /home/alpha/Models/Raw/SUSTech_SUS-Chat-34B/
  # Very low density and low weight since its a Yi 4K finetune, to try and preserve long context performance while "keeping" some of SUS
    parameters:
      weight: 0.08
      density: 0.08
merge_method: dare_ties
base_model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama
parameters:

  int8_mask: true
dtype: bfloat16
```
***
## Credits:

https://github.com/cg123/mergekit/tree/dare

https://huggingface.co/NousResearch/Nous-Capybara-34B/

https://huggingface.co/bhenrym14/airoboros-3_1-yi-34b-200k

https://huggingface.co/migtissera/Tess-M-v1.4

https://huggingface.co/kyujinpy/PlatYi-34B-200k-Q-FastChat

https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-v2

https://huggingface.co/Mihaiii/Pallas-0.4

https://huggingface.co/SUSTech/SUS-Chat-34B

https://huggingface.co/chargoddard/Yi-34B-200K-Llama

https://huggingface.co/01-ai/Yi-34B-200K