GPT-2 Open Platypus
gpt2_open-platypus is an instruction fine-tuned model based on the GPT-2 transformer architecture.
Benchmark Metrics
| Metric | lgaalves/gpt2_open-platypus | gpt2 (base) |
|---|---|---|
| Avg. | 30.01 | 29.9 |
| ARC (25-shot) | 22.18 | 21.84 |
| HellaSwag (10-shot) | 31.29 | 31.6 |
| MMLU (5-shot) | 26.19 | 25.86 |
| TruthfulQA (0-shot) | 40.35 | 40.67 |
We use state-of-the-art Language Model 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: gpt2_open-platypus is an auto-regressive language model based on the GPT-2 transformer architecture.
- Language(s): English
How to use:
# Use a pipeline as a high-level helper
>>> from transformers import pipeline
>>> pipe = pipeline("text-generation", model="lgaalves/gpt2_open-platypus")
>>> question = "What is a large language model?"
>>> answer = pipe(question)
>>> print(answer[0]['generated_text'])
or, you can load the model direclty using:
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("lgaalves/gpt2_open-platypus")
model = AutoModelForCausalLM.from_pretrained("lgaalves/gpt2_open-platypus")
Training Dataset
lgaalves/gpt2_open-platypus trained using STEM and logic based dataset garage-bAInd/Open-Platypus.
Training Procedure
lgaalves/gpt2_open-platypus was instruction fine-tuned using LoRA on 1 T4 GPU on Google Colab. It took about 27 minutes 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.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 25.16 |
| ARC (25-shot) | 22.18 |
| HellaSwag (10-shot) | 31.29 |
| MMLU (5-shot) | 26.19 |
| TruthfulQA (0-shot) | 40.35 |
| Winogrande (5-shot) | 51.3 |
| GSM8K (5-shot) | 0.15 |
| DROP (3-shot) | 4.64 |
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