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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},
}
