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---
language:
- en
license: other
library_name: transformers
tags:
- text-generation-inference
- merge
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
pipeline_tag: text-generation
model-index:
- name: CapyTessBorosYi-34B-200K-DARE-Ties
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 64.93
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 85.92
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 76.18
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 55.84
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 83.03
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 61.94
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties
      name: Open LLM Leaderboard
---

# Obsolete, see: https://huggingface.co/brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity

***

**NousResearch/Nous-Capybara-34B**, **migtissera/Tess-M-v1.3** and **bhenrym14/airoboros-3_1-yi-34b-200k** 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'


Merged 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-M-v1.3
    parameters:
      weight: 0.41
      density: 0.50
  - model: /home/alpha//Storage/Models/Raw/bhenrym14_airoboros-3_1-yi-34b-200k
    parameters:
      weight: 0.18
      density: 0.46
  - model: /home/alpha/Storage/Models/Raw/Nous-Capybara-34B
    parameters:
      weight: 0.41
      density: 0.50
merge_method: dare_ties
base_model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama
parameters:
int8_mask: true
dtype: bfloat16
```

dare_ties is testing with better perplexity than a regular ties merge with the same merge configuration. Model weights that add up to one also seem optimal from testing. And high context results seem... better than the previous dare merge with Tess 1.2.

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 (and no other samplers) if output doesn't seem right. Yi tends to run "hot" by default.

Sometimes the model "spells out" the stop token as `</s>` like Capybara, so you may need to add `</s>` as an additional stopping condition. It also might respond to the llama-2 chat format.

***
24GB GPUs can run Yi-34B-200K models at **45K-75K context** with exllamav2. 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/), and recommend exl2 quantizations on data similar to the desired task, such as these targeted at story writing: [4.0bpw](https://huggingface.co/brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties-exl2-4bpw-fiction) / [3.1bpw](https://huggingface.co/brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties-exl2-3.1bpw-fiction)
***

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-v1.3

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

https://huggingface.co/01-ai/Yi-34B-200K
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__CapyTessBorosYi-34B-200K-DARE-Ties)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |71.31|
|AI2 Reasoning Challenge (25-Shot)|64.93|
|HellaSwag (10-Shot)              |85.92|
|MMLU (5-Shot)                    |76.18|
|TruthfulQA (0-shot)              |55.84|
|Winogrande (5-shot)              |83.03|
|GSM8k (5-shot)                   |61.94|