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---
license: mit
datasets:
- garage-bAInd/Open-Platypus
- lgaalves/camel-ai-physics
language:
- en
pipeline_tag: text-generation
---
# lgaalves/gpt2_camel_physics-platypus
**lgaalves/gpt2_camel_physics-platypuss** is an instruction fine-tuned model based on the GPT-2 transformer architecture.
### Benchmark Metrics
| Metric |lgaalves/gpt2_camel_physics-platypus | gpt2 (base) |
|-----------------------|-------|-------|
| Avg. | - | 29.9 |
| ARC (25-shot) | - | 21.84 |
| HellaSwag (10-shot) | - | 31.6 |
| MMLU (5-shot) | - | 25.86 |
| TruthfulQA (0-shot) | - | 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:** **gpt2_open-platypus** is an auto-regressive language model based on the GPT-2 transformer architecture.
* **Language(s)**: English
### How to use:
```python
# Use a pipeline as a high-level helper
>>> from transformers import pipeline
>>> pipe = pipeline("text-generation", model="lgaalves/gpt2_camel_physics-platypus")
>>> question = "What is a large language model?"
>>> answer = pipe(question)
>>> print(answer[0]['generated_text'])
```
or, you can load the model direclty using:
```python
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("lgaalves/gpt2_camel_physics-platypus")
model = AutoModelForCausalLM.from_pretrained("lgaalves/gpt2_camel_physics-platypus")
```
### Training Dataset
`lgaalves/gpt2_open-platypus` trained using STEM and logic based dataset [garage-bAInd/Open-Platypus](https://huggingface.co./datasets/garage-bAInd/Open-Platypus) and
the GPT4 generated dataset [lgaalves/camel-physics](https://huggingface.co./datasets/lgaalves/camel-physics).
### Training Procedure
`lgaalves/gpt2_camel_physics-platypus` was instruction fine-tuned using LoRA on 1 v100 GPU on Google Colab. It took about 17 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](https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co./datasets/open-llm-leaderboard/details_lgaalves__gpt2_camel_physics-platypus)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 25.04 |
| ARC (25-shot) | 23.04 |
| HellaSwag (10-shot) | 31.32 |
| MMLU (5-shot) | 26.91 |
| TruthfulQA (0-shot) | 39.56 |
| Winogrande (5-shot) | 49.64 |
| GSM8K (5-shot) | 0.0 |
| DROP (3-shot) | 4.79 |
|