---
license: apache-2.0
tags:
- finetune
- fine tune
- dpo
- sft
- yi
model-index:
- name: Yi-34B-200K-AEZAKMI-RAW-1701
  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: 66.81
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=adamo1139/Yi-34B-200K-AEZAKMI-RAW-1701
      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: 85.79
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=adamo1139/Yi-34B-200K-AEZAKMI-RAW-1701
      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: 75.44
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=adamo1139/Yi-34B-200K-AEZAKMI-RAW-1701
      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: 57.91
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=adamo1139/Yi-34B-200K-AEZAKMI-RAW-1701
      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: 80.35
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=adamo1139/Yi-34B-200K-AEZAKMI-RAW-1701
      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: 59.97
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=adamo1139/Yi-34B-200K-AEZAKMI-RAW-1701
      name: Open LLM Leaderboard
---

THIS MODEL IS EXPERIMENTAL AND MIGHT BE BUGGY, I DIDN'T PERFECT THE STRENGTH OF DPO AND SFT YET. 


Yi-34B-200K trained via DPO on RAWrr_v1 at ctx 200 (lora_r 4, lora_alpha 8) and then via SFT at ctx 1400 (lora_r 16, lora_alpha 32) on AEZAKMI_v2.
It's less prone to refusals than Yi-34B-200K-AEZAKMI-v2 but that's work in progress still - I want to do DPO with higher lora rank and ctx and then repeat SFT training.
I haven't tested it too much, but on what I've seen, it's a good model. 

If you want to re-produce this model by merging loras, start by downloading Yi-34B-200K-Llamafied. \
Then merge it with https://huggingface.co/adamo1139/Yi-34B-200K-rawrr1-LORA-DPO-experimental-r2 \
Then merge the resulting model with https://huggingface.co/adamo1139/yi-34b-200k-aezakmi-v2-rawrr-v1-run1-experimental-LoRA


License:
apache-2.0
# [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_adamo1139__Yi-34B-200K-AEZAKMI-RAW-1701)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |71.04|
|AI2 Reasoning Challenge (25-Shot)|66.81|
|HellaSwag (10-Shot)              |85.79|
|MMLU (5-Shot)                    |75.44|
|TruthfulQA (0-shot)              |57.91|
|Winogrande (5-shot)              |80.35|
|GSM8k (5-shot)                   |59.97|