transformers safetensors mistral text-generation alignment-handbook generated_from_trainer juanako UNA conversational dataset:HuggingFaceH4/ultrafeedback_binarized arxiv:2109.07958 arxiv:2310.16944 arxiv:2305.18290 license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
datasets:
- HuggingFaceH4/ultrafeedback_binarized
license: apache-2.0
tags:
- alignment-handbook
- generated_from_trainer
- juanako
- mistral
- UNA
model-index:
- name: juanako-7b-UNA
  results:
  - task:
      type: text-generation
      name: TruthfulQA (MC2)
    dataset:
      name: truthful_qa
      type: text-generation
      config: multiple_choice
      split: validation
    metrics:
    - type: accuracy
      value: 65.13
      verified: true
  - task:
      type: text-generation
      name: ARC-Challenge
    dataset:
      name: ai2_arc
      type: text-generation
      config: ARC-Challenge
      split: test
    metrics:
    - type: accuracy
      value: 68.17
      verified: true
  - task:
      type: text-generation
      name: HellaSwag
    dataset:
      name: Rowan/hellaswag
      type: text-generation
      split: test
    metrics:
    - type: accuracy
      value: 85.34
      verified: true
    - type: accuracy
      value: 83.57
  - task:
      type: text-generation
      name: Winogrande
    dataset:
      name: winogrande
      type: text-generation
      config: winogrande_debiased
      split: test
    metrics:
    - type: accuracy
      value: 78.85
      verified: true
  - task:
      type: text-generation
      name: MMLU
    dataset:
      name: cais/mmlu
      type: text-generation
      config: all
      split: test
    metrics:
    - type: accuracy
      value: 62.47
      verified: true
  - task:
      type: text-generation
      name: DROP
    dataset:
      name: drop
      type: text-generation
      split: validation
    metrics:
    - type: accuracy
      value: 38.74
      verified: true
  - task:
      type: text-generation
      name: PubMedQA
    dataset:
      name: bigbio/pubmed_qa
      type: text-generation
      config: pubmed_qa_artificial_bigbio_qa
      split: validation
    metrics:
    - type: accuracy
      value: 76.0
  - 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: 68.17
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/juanako-7b-UNA
      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: 85.34
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/juanako-7b-UNA
      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: 62.47
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/juanako-7b-UNA
      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: 65.13
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/juanako-7b-UNA
      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: 78.85
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/juanako-7b-UNA
      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: 44.81
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/juanako-7b-UNA
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 48.37
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/juanako-7b-UNA
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 30.42
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/juanako-7b-UNA
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 2.87
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/juanako-7b-UNA
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 6.15
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/juanako-7b-UNA
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 17.16
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/juanako-7b-UNA
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 19.68
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/juanako-7b-UNA
      name: Open LLM Leaderboard
---

# juanako-7b-UNA (Uniform Neural Alignment)

This model is a fine-tuned version of [fblgit/juanako-7b-UNA-v2-phase-1](https://huggingface.co/fblgit/juanako-7b-UNA-v2-phase-1) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It outperforms in many aspects most of the current Mistral based models and is the **latest and most powerful juanako version as of now**.

## Scores

The official HuggingFace results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/results/blob/main/fblgit/juanako-7b-UNA/results_2023-11-28T08-33-33.965228.json)

| Model | Average ⬆️| ARC (25-s) ⬆️ | HellaSwag (10-s) ⬆️ | MMLU (5-s) ⬆️| TruthfulQA (MC) (0-s) ⬆️ | Winogrande (5-s) | GSM8K (5-s) | DROP (3-s) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
|[mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 50.32 | 59.58  | 83.31  | 64.16  | 42.15 | 78.37 | 18.12 | 6.14 |
| [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1) | 59.0 | 66.21 | 83.64 | 62.37  | 59.65 | 78.14 | 19.56 | 43.84 |
| [fblgit/juanako-7b-UNA](https://huggingface.co/fblgit/juanako-7b-UNA) | **59.91** | **68.17** | **85.34** | 62.47  | **65.13** | **78.85** | **20.7** | 38.74 |

It scores: **59.91** according HuggingFace LLM Leaderboard.
It scores: **65.1** with `big-refactor` branch of lm-eval-harness

Author [Xavier M.](mailto:xavi@juanako.ai) @fblgit

## Model description

juanako uses UNA, Uniform Neural Alignment. A training technique that ease alignment between transformer layers yet to be published.

### Prompts
The following prompts showed positive results, it may depend the task and needs further experimentation but this should work for starters:
```
<|im_start|>system
- You are a helpful assistant chatbot trained by MosaicML.
- You answer questions.
- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>
<|im_start|>user
Explain QKV<|im_end|>
<|im_start|>assistant
```
```
### Assistant: I am StableVicuna, a large language model created by CarperAI. I am here to chat!

### Human: Explain QKV
### Assistant:
```
```
[Round <|round|>]
问：Explain QKV
答：
```
```
[Round <|round|>]
Question：Explain QKV
Answer：
```
```
Question：Explain QKV
Answer：
```

## Evaluations (lm-eval big-refactor branch)

### TruthfulQA 0-Shot
```
|    Tasks     |Version|Filter|Metric|Value |   |Stderr|
|--------------|-------|------|------|-----:|---|-----:|
|truthfulqa_mc2|Yaml   |none  |acc   |0.6549|±  |0.0153|
```
### ARC 25-Shot
```
|    Tasks    |Version|Filter| Metric |Value |   |Stderr|
|-------------|-------|------|--------|-----:|---|-----:|
|arc_challenge|Yaml   |none  |acc     |0.6476|±  |0.0140|
|             |       |none  |acc_norm|0.6809|±  |0.0136|
```
### HellaSwag 10-Shot
```
|  Tasks  |Version|Filter| Metric |Value |   |Stderr|
|---------|-------|------|--------|-----:|---|-----:|
|hellaswag|Yaml   |none  |acc     |0.6703|±  |0.0047|
|         |       |none  |acc_norm|0.8520|±  |0.0035|
```
### GSM8k 5-Shot
```
|Tasks|Version|  Filter  |  Metric   |Value |   |Stderr|
|-----|-------|----------|-----------|-----:|---|-----:|
|gsm8k|Yaml   |get-answer|exact_match|0.4898|±  |0.0138|
```
### GPT Evaluations 0-Shot
```
|    Tasks     |Version|Filter|  Metric  |Value |   |Stderr|
|--------------|-------|------|----------|-----:|---|-----:|
|boolq         |Yaml   |none  |acc       |0.8703|±  |0.0059|
|lambada_openai|Yaml   |none  |perplexity|3.2598|±  |0.0705|
|              |       |none  |acc       |0.7336|±  |0.0062|
|piqa          |Yaml   |none  |acc       |0.8254|±  |0.0089|
|              |       |none  |acc_norm  |0.8292|±  |0.0088|
|sciq          |Yaml   |none  |acc       |0.9580|±  |0.0063|
|              |       |none  |acc_norm  |0.9130|±  |0.0089|
```
### MathQA 0-Shot
```
|Tasks |Version|Filter| Metric |Value |   |Stderr|
|------|-------|------|--------|-----:|---|-----:|
|mathqa|Yaml   |none  |acc     |0.3752|±  |0.0089|
|      |       |none  |acc_norm|0.3772|±  |0.0089|
```
### PiQa 1-Shot
```
|Tasks|Version|Filter| Metric |Value |   |Stderr|
|-----|-------|------|--------|-----:|---|-----:|
|piqa |Yaml   |none  |acc     |0.8308|±  |0.0087|
|     |       |none  |acc_norm|0.8357|±  |0.0086|
```
### Winogrande 5-Shot
```
|  Tasks   |Version|Filter|Metric|Value|   |Stderr|
|----------|-------|------|------|----:|---|-----:|
|winogrande|Yaml   |none  |acc   |0.768|±  |0.0119|
```
### PubMedQA 0-Shot
```
| Tasks  |Version|Filter|Metric|Value|   |Stderr|
|--------|-------|------|------|----:|---|-----:|
|pubmedqa|Yaml   |none  |acc   | 0.76|±  |0.0191|
```
### RACE 1-Shot
```
|Tasks|Version|Filter|Metric|Value |   |Stderr|
|-----|-------|------|------|-----:|---|-----:|
|race |Yaml   |none  |acc   |0.5282|±  |0.0154|
```
### MMLU 5-Shot (8-Bit)
```
|      Groups      |Version|Filter|Metric|Value |   |Stderr|
|------------------|-------|------|------|-----:|---|-----:|
|mmlu              |N/A    |none  |acc   |0.6137|±  |0.1243|
| - humanities     |N/A    |none  |acc   |0.5671|±  |0.1101|
| - other          |N/A    |none  |acc   |0.6859|±  |0.1164|
| - social_sciences|N/A    |none  |acc   |0.7195|±  |0.0713|
| - stem           |N/A    |none  |acc   |0.5087|±  |0.1297|
```
### DROP 3-Shot (8-Bit) (Instruct-Eval)
```
{'score': 0.49801113762927607}
{'drop': 49.8}
drop: 49.8
```

### CRASS 0-Shot (Instruct-Eval)
```
{'score': 0.8357664233576643}
{'crass': 83.58}
crass: 83.58
```

## Training Details

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 14
- gradient_accumulation_steps: 16
- total_train_batch_size: 224
- total_eval_batch_size: 14
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.4795        | 0.2   | 56   | 0.4958          | -1.3684        | -2.6385          | 0.7552             | 1.2701          | -265.3887      | -241.2612    | -2.2572         | -2.4922       |
| 0.4642        | 0.4   | 112  | 0.4859          | -1.0380        | -1.9769          | 0.7273             | 0.9389          | -258.7718      | -237.9569    | -2.2414         | -2.4751       |
| 0.4758        | 0.61  | 168  | 0.4808          | -1.2594        | -2.3704          | 0.7343             | 1.1110          | -262.7074      | -240.1708    | -2.2305         | -2.4633       |
| 0.4549        | 0.81  | 224  | 0.4768          | -1.1906        | -2.3201          | 0.7552             | 1.1295          | -262.2044      | -239.4827    | -2.2284         | -2.4610       |


### Framework versions

- Transformers 4.35.0-UNA
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1

## Citations
If you find juanako useful please:

```
@misc{juanako7buna,
  title={Juanako: Uniform Neural Alignment}, 
  author={Xavier Murias},
  year={2023},
  publisher = {HuggingFace},
  journal = {HuggingFace repository},
  howpublished = {\url{https://huggingface.co/fblgit/juanako-7b-UNA}},
}
```

Thanks to all the brilliant humans behind the creation of AI, here some of the ones that we find relevant to our research. If you feel a citation is missing, please contact.
```
@misc{lin2021truthfulqa,
  title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},
  author={Stephanie Lin and Jacob Hilton and Owain Evans},
  year={2021},
  eprint={2109.07958},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}
@misc{tunstall2023zephyr,
      title={Zephyr: Direct Distillation of LM Alignment}, 
      author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf},
      year={2023},
      eprint={2310.16944},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@inproceedings{Bisk2020,
  author = {Yonatan Bisk and Rowan Zellers and
            Ronan Le Bras and Jianfeng Gao
            and Yejin Choi},
  title = {PIQA: Reasoning about Physical Commonsense in
           Natural Language},
  booktitle = {Thirty-Fourth AAAI Conference on
               Artificial Intelligence},
  year = {2020},
}
@software{eval-harness,
  author       = {Gao, Leo and
                  Tow, Jonathan and
                  Biderman, Stella and
                  Black, Sid and
                  DiPofi, Anthony and
                  Foster, Charles and
                  Golding, Laurence and
                  Hsu, Jeffrey and
                  McDonell, Kyle and
                  Muennighoff, Niklas and
                  Phang, Jason and
                  Reynolds, Laria and
                  Tang, Eric and
                  Thite, Anish and
                  Wang, Ben and
                  Wang, Kevin and
                  Zou, Andy},
  title        = {A framework for few-shot language model evaluation},
  month        = sep,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {v0.0.1},
  doi          = {10.5281/zenodo.5371628},
  url          = {https://doi.org/10.5281/zenodo.5371628}
}
@misc{rafailov2023direct,
    title={Direct Preference Optimization: Your Language Model is Secretly a Reward Model}, 
    author={Rafael Rafailov and Archit Sharma and Eric Mitchell and Stefano Ermon and Christopher D. Manning and Chelsea Finn},
    year={2023},
    eprint={2305.18290},
    archivePrefix={arXiv},
}
```

# [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_fblgit__juanako-7b-UNA)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |67.46|
|AI2 Reasoning Challenge (25-Shot)|68.17|
|HellaSwag (10-Shot)              |85.34|
|MMLU (5-Shot)                    |62.47|
|TruthfulQA (0-shot)              |65.13|
|Winogrande (5-shot)              |78.85|
|GSM8k (5-shot)                   |44.81|


# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__juanako-7b-UNA)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |20.77|
|IFEval (0-Shot)    |48.37|
|BBH (3-Shot)       |30.42|
|MATH Lvl 5 (4-Shot)| 2.87|
|GPQA (0-shot)      | 6.15|
|MuSR (0-shot)      |17.16|
|MMLU-PRO (5-shot)  |19.68|

