metadata

license: other
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
language:
  - en
library_name: transformers
base_model: []
tags:
  - mergekit
  - merge
  - Yi

Yi 34B Merge v7

A merge of several Yi 34B 200K models using the new DARE Ties method via mergekit. The goal is to create a merge model that excels at 32K+ context performance.

Prompt template: Orca-Vicuna

SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:

It might recognize ChatML, and possibly Alpaca-like formats. Raw prompting as described here is also effective: https://old.reddit.com/r/LocalLLaMA/comments/18zqy4s/the_secret_to_writing_quality_stories_with_llms/

Running

Being a Yi model, try running a lower temperature with 0.02-0.06 MinP, a little repetition penalty, maybe mirostat with a low tau, and no other samplers. Yi tends to run "hot" by default, and it really needs a low temperature + MinP to cull the huge vocabulary.

24GB GPUs can efficiently run Yi-34B-200K models at 45K-90K context with exllamav2, and performant UIs like exui. I go into more detail in this post. 16GB GPUs can still run the high context with aggressive quantization.

I recommend exl2 quantizations profiled on data similar to the desired task. It is especially sensitive to the quantization data at low bpw. I've uploaded my own fiction-oriented quantizations here: https://huggingface.co/collections/brucethemoose/most-recent-merge-65742644ca03b6c514afa204

To load/train this in full-context backends like transformers, you must change max_position_embeddings in config.json to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends like exllamav2 or unsloth.

Testing Notes

See: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-merge-v5#testing-notes

A "4k" merge model was created to try and extend the context of SUS Chat and DPO-bagel before adding them to the merge: https://huggingface.co/brucethemoose/SUS-Bagel-200K-DARE-Test

In addition, the weight gradients are biased towards Vicuna-format models in the first few layers to try and "emphasize" the Orca-Vicuna prompt template. How sucessful this is remains to be seen.

Merge Method

This model was merged using the DARE TIES merge method using /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

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
    parameters:
      weight: [0.23, 0.125, 0.125, 0.125, 0.125, 0.125]
      density: 0.59
  - model: /home/alpha/Models/Raw/Mihaiii_Pallas-0.5
    parameters:
      weight: [0.23, 0.125, 0.125, 0.125, 0.125, 0.125]
      density: 0.59
  - model: /home/alpha//Storage/Models/Raw/bhenrym14_airoboros-3_1-yi-34b-200k
    parameters:
      weight: [0.02, 0.106, 0.106, 0.106, 0.106, 0.106]
      density: 0.59
  - model: /home/alpha/Storage/Models/Raw/jondurbin_bagel-34b-v0.2
    #Only the SFT in the main merge since the DPO version seems to have no long context ability at all
    parameters:
      weight: [0.02, 0.100, 0.100, 0.100, 0.100, 0.100]
      density: 0.4
  - model: /home/alpha/Storage/Models/Raw/kyujinpy_PlatYi-34B-200k-Q-FastChat
    parameters:
      weight: [0.02, 0.100, 0.100, 0.100, 0.100, 0.100]
      density: 0.59
  #- model: /home/alpha/Storage/Models/Raw/ehartford_dolphin-2.2-yi-34b-200k
  #  Dolphin 200K seems to be funky 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: [0.02, 0.110, 0.110, 0.110, 0.110, 0.110]
      density: 0.59
  - model: /home/alpha/Storage/Models/Raw/Nous-Capybara-34B
    parameters:
      weight:  [0.22, 0.126, 0.126, 0.126, 0.126, 0.126]
      density: 0.59
  - model: /home/alpha/Storage/Models/Raw/4kmerge
    parameters:
      weight: [0.02,  0.108, 0.108, 0.108, 0.108, 0.108]
      density: 0.5
  - model: /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0
    parameters:
      weight: [0.22, 0.100, 0.100, 0.100, 0.100, 0.10]
      density: 0.59
merge_method: dare_ties
tokenizer_source: union
base_model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama
parameters:
  int8_mask: true
dtype: bfloat16

The following config was used for the "4kmerge" model:

models:
  - model: /home/alpha/Models/Raw/chargoddard_Yi-34B-Llama
  # No parameters necessary for base model
  - model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama
    parameters:
      weight: 0.5
      density: 1
  - model: /home/alpha/Models/Raw/SUSTech_SUS-Chat-34B
    parameters:
      weight: 0.2
      density: 0.12
  - model: /home/alpha/Models/Raw/jondurbin_bagel-dpo-34b-v0.2
    parameters:
      weight: 0.2
      density: 0.15
  - model: /home/alpha/Models/Raw/jondurbin_bagel-34b-v0.2
    parameters:
      weight: 0.1
      density: 0.12
merge_method: dare_ties
tokenizer_source: union
base_model: /home/alpha/Models/Raw/chargoddard_Yi-34B-Llama
parameters:
  int8_mask: true
dtype: bfloat16