pytorch tensorboard bloom text-generation transformers generated_from_trainer chatbot license:bigscience-bloom-rail-1.0
---
license: bigscience-bloom-rail-1.0
tags:
- generated_from_trainer
- chatbot
widget:
- text: "If you could live anywhere, where would it be? peter szemraj:" 
  example_title: "live anywhere"
- text: "What would you sing at Karaoke night? peter szemraj:" 
  example_title: "Karaoke"
- text: "If you could hire someone to help you, would it be with cleaning, cooking, or yard work? peter szemraj:" 
  example_title: "help"
- text: "What form of public transportation do you prefer? (air, boat, train, bus, car, etc.) peter szemraj:" 
  example_title: "transportation"
- text: "What's your favorite zoo animal? peter szemraj:" 
  example_title: "animal"
- text: "Do you like or dislike surprises? Why or why not? peter szemraj:" 
  example_title: "surprises"
- text: "What celebrity would you like to meet at Starbucks for a cup of coffee? peter szemraj:" 
  example_title: "celebrity "
- text:: "qu'est-il arrivé à Calvin Miller pour que son pénis soit réduit à la taille d'un réticulum endoplasmique moyen dans une cellule animale?"
  example_title: "French science"
  
inference:
  parameters:
    min_length: 2
    max_length: 64
    length_penalty: 0.7
    temperature: 0.3
    no_repeat_ngram_size: 2
    top_k: 20
    do_sample: True
    repetition_penalty: 4.5
---

# blooming-pierre-350m

This model is a fine-tuned version of [bigscience/bloom-350m](https://huggingface.co/bigscience/bloom-350m) on approx 80k messages (mine).

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 2

### Training results



### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
