README.md

---
model-index:
- name: Miqu-70B-Alpaca-DPO
  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: 73.21
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Undi95/Miqu-70B-Alpaca-DPO
      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: 88.6
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Undi95/Miqu-70B-Alpaca-DPO
      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: 75.41
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Undi95/Miqu-70B-Alpaca-DPO
      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: 69.44
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Undi95/Miqu-70B-Alpaca-DPO
      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: 85.4
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Undi95/Miqu-70B-Alpaca-DPO
      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: 67.55
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Undi95/Miqu-70B-Alpaca-DPO
      name: Open LLM Leaderboard
---

## Miqu DPO

Miqu DPO is the same model than Miqu, with a DPO trained on MiquMaid v2 on Alpaca format, it was done for the purpose to try to uncensor further Miqu and make Alpaca prompt more usable with base Miqu. Also, this will be one of the base for MiquMaid-v2-2x70B-DPO.

Miqu base is REALLY censored outside RP, this LoRA let him reply a little more thing, but that's it. To have his full potential, it need to be in a merge/MoE of MiquMaid, since the loRA was based for MiquMaid, not Miqu base. I still let it public for who want it.

It uncensor a little the model, but keep some warning. Sometime reply really unethically.

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## Description

This repo contains FP16 files of Miqu-70B-DPO.

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<!-- description start -->
## Dataset used

- NobodyExistsOnTheInternet/ToxicDPOqa
- Undi95/toxic-dpo-v0.1-NoWarning

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<!-- prompt-template start -->
## Prompt format: Alpaca
```
### Instruction:
{prompt}

### Input:
{input}

### Response:
{output}
```

Or simple Mistral format (but the uncensoring was done on Alpaca, so Alpaca is recommanded).

## Others

If you want to support me, you can [here](https://ko-fi.com/undiai).
# [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_Undi95__Miqu-70B-Alpaca-DPO)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |76.60|
|AI2 Reasoning Challenge (25-Shot)|73.21|
|HellaSwag (10-Shot)              |88.60|
|MMLU (5-Shot)                    |75.41|
|TruthfulQA (0-shot)              |69.44|
|Winogrande (5-shot)              |85.40|
|GSM8k (5-shot)                   |67.55|