README.md

---
license: apache-2.0
base_model: BEE-spoke-data/smol_llama-101M-GQA
tags:
- art
- text2image
- prompt
- prompt generator
- diffusion util
metrics:
- accuracy
inference:
  parameters:
    max_new_tokens: 64
    do_sample: true
    temperature: 0.8
    repetition_penalty: 1.15
    no_repeat_ngram_size: 4
    eta_cutoff: 0.001
    renormalize_logits: true
widget:
- text: avocado chair
  example_title: avocado chair
- text: A mysterious potato
  example_title: potato
pipeline_tag: text-generation
datasets:
- pszemraj/midjourney-messages-cleaned
---

# smol_llama-101M-midjourney-messages

Given a 'partial prompt' for a text2image model, this generates additional relevant text to include for a full prompt.


![example](https://i.imgur.com/f2hzgq1.png)

dalle3:


![image/png](https://cdn-uploads.huggingface.co/production/uploads/60bccec062080d33f875cd0c/PIBazuqQ1DrTxZbWSay2o.png)


## Model description

This model is a fine-tuned version of [BEE-spoke-data/smol_llama-101M-GQA](https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA) on the `pszemraj/midjourney-messages-cleaned` dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8431
- Accuracy: 0.4682


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.00025
- train_batch_size: 4
- eval_batch_size: 4
- seed: 17056
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: inverse_sqrt
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1.0