juanako-7b-UNA (Uniform Neural Alignment)

This model is a fine-tuned version of 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

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	50.32	59.58	83.31	64.16	42.15	78.37	18.12	6.14
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	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. @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

Detailed results can be found here

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

Detailed results can be found here

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

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Safetensors

Model size

7.24B params

Tensor type

BF16

Inference Examples

Text Generation

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Evaluation results

accuracy on truthful_qa
validation set self-reported

65.130
accuracy on ai2_arc
test set self-reported

68.170
accuracy on Rowan/hellaswag
test set self-reported

85.340
accuracy on Rowan/hellaswag
test set self-reported

83.570
accuracy on winogrande
test set self-reported

78.850
accuracy on cais/mmlu
test set self-reported

62.470
accuracy on drop
validation set self-reported

38.740
accuracy on bigbio/pubmed_qa
validation set self-reported

76.000
normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

68.170
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

85.340

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