|
--- |
|
license: mit |
|
datasets: |
|
- databricks/databricks-dolly-15k |
|
language: |
|
- en |
|
pipeline_tag: text-generation |
|
--- |
|
|
|
|
|
|
|
# GPT-2-dolly |
|
|
|
**GPT-2-dolly** is an instruction fine-tuned model based on the GPT-2 transformer architecture. |
|
|
|
|
|
### Benchmark Metrics |
|
|
|
| Metric | GPT-2-dolly | GPT-2 (base) | |
|
|-----------------------|-------|-------| |
|
| Avg. | 29.85 | **29.99** | |
|
| ARC (25-shot) | 21.76 | **21.84** | |
|
| HellaSwag (10-shot) | 30.77 | **31.6** | |
|
| MMLU (5-shot) | 24.66 | **25.86** | |
|
| TruthfulQA (0-shot) | **42.22** | 40.67 | |
|
|
|
|
|
|
|
We use state-of-the-art [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard. Please see below for detailed instructions on reproducing benchmark results. |
|
|
|
### Model Details |
|
|
|
* **Trained by**: Luiz G A Alves |
|
* **Model type:** **GPT-2-dolly** is an auto-regressive language model based on the GPT-2 transformer architecture. |
|
* **Language(s)**: English |
|
|
|
### Prompt Template |
|
``` |
|
### Instruction: |
|
|
|
<prompt> (without the <>) |
|
|
|
### Response: |
|
``` |
|
|
|
### Training Dataset |
|
|
|
`lgaalves/gpt2-dolly` trained using the Databricks Dolly dataset [`garage-bAInd/Open-Platypus`](https://huggingface.co./datasets/garage-bAInd/Open-Platypus). |
|
|
|
|
|
### Training Procedure |
|
|
|
`lgaalves/gpt2-dolly` was instruction fine-tuned using LoRA on 1 T4 GPU on Google Colab. It took about 1.5 hours to train it. |
|
|
|
|
|
# Intended uses, limitations & biases |
|
|
|
You can use the raw model for text generation or fine-tune it to a downstream task. The model was not extensively tested and may produce false information. It contains a lot of unfiltered content from the internet, which is far from neutral. |
|
|
|
|
