File size: 5,778 Bytes
aaf2464 93d76bc fbe8884 93d76bc aaf2464 57405a4 aaf2464 57405a4 aaf2464 d056075 aaf2464 d056075 aaf2464 3babe1b aaf2464 d056075 aaf2464 d056075 aaf2464 d056075 aaf2464 |
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 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 |
---
license: apache-2.0
pipeline_tag: text-generation
language:
- en
tags:
- pretrained
datasets:
- Skylion007/openwebtext
- c4
- wikimedia/wikipedia
- tiiuae/falcon-refinedweb
- izumi-lab/open-text-books
- togethercomputer/RedPajama-Data-V2
- databricks/databricks-dolly-15k
- euclaise/reddit-instruct-curated
- CohereForAI/aya_dataset
widget:
- messages:
- role: user
content: Specs of a game about trolls and warriors in a fantasy world.
- messages:
- role: user
content: Reducing waste generation is essential to...
- messages:
- role: user
content: Water, planet, resource, future
- messages:
- role: user
content: Background story of an RPG game about wizards and dragons in a sci-fi world. The story takes place in a...
inference:
parameters:
max_new_tokens: 250
do_sample: true
temperature: 0.65
top_p: 0.55
top_k: 35
repetition_penalty: 1.176
---
# Minueza-32M-Base
## Summary
Minueza-32M-Base is a foundation model with 32 million parameters trained from scratch on a large corpus of text in English.
It's available in the following formats: [Safetensors](https://huggingface.co/Felladrin/Minueza-32M-Base), [GGUF](https://huggingface.co/Felladrin/gguf-Minueza-32M-Base), and [ONNX](https://huggingface.co/Felladrin/onnx-Minueza-32M-Base).
And it's being released alongside these fine-tuned versions:
- [Minueza-32M-UltraChat](https://huggingface.co/Felladrin/Minueza-32M-UltraChat): Trained in a single conversational dataset.
- [Minueza-32M-Chat](https://huggingface.co/Felladrin/Minueza-32M-Chat): Trained in a mix of conversational datasets.
## Intended Uses
This model was created with the following objectives in mind:
- Run on mobile web browsers via [Transformers.js](https://huggingface.co/docs/transformers.js).
- Run fast on machines without GPU.
- Serve as a base for fine-tunes using ChatML format, hence the two additional special tokens (`<|im_start|>` and `<|im_end|>`) with `<|im_end|>` as default EOS token.
- ChatML works great for both instruction and chat models, so if all fine-tunes are made following the ChatML pattern, other users might benefit from the easiness of creating merges.
## Datasets
The model was trained on a subset of each of the following non-synthetic datasets:
- [Skylion007/openwebtext](https://huggingface.co/datasets/Skylion007/openwebtext)
- [c4](https://huggingface.co/datasets/c4)
- [wikimedia/wikipedia - 20231101.simple](https://huggingface.co/datasets/wikimedia/wikipedia/viewer/20231101.simple)
- [tiiuae/falcon-refinedweb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
- [izumi-lab/open-text-books](https://huggingface.co/datasets/izumi-lab/open-text-books)
- [togethercomputer/RedPajama-Data-V2](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2)
- [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)
- [euclaise/reddit-instruct-curated](https://huggingface.co/datasets/euclaise/reddit-instruct-curated)
- [CohereForAI/aya_dataset - original english annotations](https://huggingface.co/datasets/CohereForAI/aya_dataset/viewer/default/train?f[language_code][value]=%27eng%27)
The subsets were interleaved to form the final training corpus of approximately 650 million tokens.
## Model Architecture
This is a transformer model with the Mistral architecture, trained on a context window of 2048 tokens.
| Configuration | Value |
| :---------------------- | :---- |
| max_position_embeddings | 2048 |
| hidden_size | 312 |
| intermediate_size | 1092 |
| num_attention_heads | 12 |
| num_hidden_layers | 10 |
| num_key_value_heads | 4 |
| vocab_size | 32002 |
The pretraining was made with these hyperparameters and frameworks:
| Hyperparameter | Value |
| :-------------------------- | :-------------------------------------------- |
| learning_rate | 5e-05 |
| train_batch_size | 1 |
| eval_batch_size | 1 |
| seed | 42 |
| gradient_accumulation_steps | 8 |
| total_train_batch_size | 8 |
| optimizer | Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| lr_scheduler_type | linear |
| Framework | Version |
| :----------- | :---------- |
| Transformers | 4.38.0.dev0 |
| Pytorch | 2.1.2 |
| Datasets | 2.16.1 |
| Tokenizers | 0.15.1 |
## Usage
This is just a base model. For your task, you will likely want to perform application-specific fine-tuning as recommended above.
Also note that this model was trained on internet text data, which may contain biases, offensive or inappropriate content, and may produce incorrect or irrelevant responses. No evaluation has been conducted, so use with care.
Having that said, here's how you can run it:
```python
from transformers import pipeline
generate = pipeline("text-generation", "Felladrin/Minueza-32M-Base")
prompt = "The best way to improve your health is"
output = generate(
prompt,
max_new_tokens=256,
do_sample=True,
temperature=0.72,
top_p=0.73,
top_k=50,
repetition_penalty=1.176,
)
print(output[0]["generated_text"])
```
## License
This model is licensed under the [Apache License 2.0](https://huggingface.co/Felladrin/Minueza-32M-Base/resolve/main/license.txt).
|