File size: 2,040 Bytes
bfb6abe bfa6d6d bfb6abe bfa6d6d b4d0e1d bfa6d6d 3d50205 bfb6abe 71e37d7 bfb6abe 71e37d7 bfb6abe 2d66b0f 966ec27 94faa3c bfb6abe 70f6c54 bfa6d6d bfb6abe 71e37d7 bfb6abe 2d66b0f bfa6d6d bfb6abe |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
---
license: apache-2.0
tags:
- generated_from_trainer
- text-generation
- opt
- non-commercial
- dialogue
- chatbot
inference: false
---
# pszemraj/opt-peter-2.7B
This model is a fine-tuned version of [facebook/opt-2.7b](https://huggingface.co./facebook/opt-2.7b) on about 80k whatsapp/text messages (mine). Please use responsibly :)
Test it out on Google Colab [here](https://colab.research.google.com/gist/pszemraj/26a69775c9d012051396ab5ae980f5c1/example-text-gen-pszemraj-opt-peter-2-7b.ipynb)!
## Model description
- Exploring to see how OPT does in terms of dialogue/conversational applications
- Seems to do a lot better than GPT-Neo with similar training parameters
## Intended uses & limitations
> The base model has a custom license which propogates to this one. Most importantly, it cannot be used commercially. Read more here: [facebook/opt-2.7b](https://huggingface.co./facebook/opt-2.7b)
- the model is probably too large to use via API here. Use in Python with GPU RAM / CPU RAM > 12 gb, Colab notebook linked above.
- alternatively, you can message [a bot on telegram](http://t.me/GPTPeter_bot) where I test LLMs for dialogue generation
- **any statements or claims made by this model do not reflect actual claims/statements by me.** Keep in mind it is a _fine-tuned_ version of the model on my data, so things from pre-training are also present in outputs.
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.10.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|