---
license: apache-2.0
tags:
- alignment-handbook
- generated_from_trainer
- juanako
- mistral
- UNA
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: juanako-7b-UNA
  results:
  - task:
      type: text-generation
      name: TruthfulQA (MC2)
    dataset:
      type: text-generation
      name: truthful_qa
      config: multiple_choice
      split: validation
    metrics:
      - type: accuracy
        value: 65.49
  - task:
      type: text-generation
      name: ARC-Challenge
    dataset:
      type: text-generation
      name: ai2_arc
      config: ARC-Challenge
      split: test
    metrics:
      - type: accuracy
        value: 68.09
  - task:
      type: text-generation
      name: HellaSwag
    dataset:
      type: text-generation
      name: Rowan/hellaswag
      split: test
    metrics:
      - type: accuracy
        value: 85.20
  - task:
      type: text-generation
      name: GSM8k
    dataset:
      type: text-generation
      name: gsm8k
      config: main
      split: test
    metrics:
      - type: accuracy
        value: 48.98
  - task:
      type: text-generation
      name: Winogrande
    dataset:
      type: text-generation
      name: winogrande
      config: winogrande_debiased
      split: test
    metrics:
      - type: accuracy
        value: 76.8
  - task:
      type: text-generation
      name: MMLU
    dataset:
      type: text-generation
      name: cais/mmlu
      config: all
      split: test
    metrics:
      - type: accuracy
        value: 61.37
  - task:
      type: text-generation
      name: PiQA
    dataset:
      type: text-generation
      name: piqa
      split: test
    metrics:
      - type: accuracy
        value: 83.57
  - task:
      type: text-generation
      name: DROP
    dataset:
      type: text-generation
      name: drop
      split: validation
    metrics:
      - type: accuracy
        value: 49.8
  - task:
      type: text-generation
      name: PubMedQA
    dataset:
      type: text-generation
      name: bigbio/pubmed_qa
      config: pubmed_qa_artificial_bigbio_qa
      split: validation
    metrics:
      - type: accuracy
        value: 76.0
---

# juanako-7b-UNA-v2

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**.

## Scoring and records (26-November-2023)
Here are some results:
* Scores #1 7B Model
* Scores #4 GSM8k
* Scores #2 in TruthfulQA
* Scores #6 in CoPa
* Scores #2 in PiQA
* Scores #9 in BoolQ

Many evaluations were performed, but it behaves very balanced in multiple fields. Feel free to submit more evaluation results.

It scores: **65.1** according HuggingFace LLM Leaderboard. 

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.

## 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 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}},
}
```

```
@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}
}
@article{cobbe2021gsm8k,
  title={Training Verifiers to Solve Math Word Problems},
  author={Cobbe, Karl and Kosaraju, Vineet and Bavarian, Mohammad and Chen, Mark and Jun, Heewoo and Kaiser, Lukasz and Plappert, Matthias and Tworek, Jerry and Hilton, Jacob and Nakano, Reiichiro and Hesse, Christopher and Schulman, John},
  journal={arXiv preprint arXiv:2110.14168},
  year={2021}
}
@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},
}
```