---
license: mit
datasets:
- lgaalves/camel-physics
language:
- en
pipeline_tag: text-generation
---



# gpt2-xl-camel-ai-physics (1.5B)

**lgaalves/gpt2-xl_camel-ai-physics** is an instruction fine-tuned model based on the GPT-2 transformer architecture.


### Benchmark Metrics

| Metric                |lgaalves/gpt2-xl_camel-ai-physics |gpt2-xl (base) |
|-----------------------|-------|-------|
| Avg.                  | 36.51 | **36.66** |
| ARC (25-shot)         | 29.52 | **30.29** |
| HellaSwag (10-shot)   | 50.62 | **51.38** |
| MMLU (5-shot)         | **26.79** | 26.43 |
| TruthfulQA (0-shot)   | **39.12** | 38.54 |


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:**  **lgaalves/gpt2-xl_camel-ai-physics** 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-xl_camel-ai-physics")
>>> 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-xl_camel-ai-physics")
model = AutoModelForCausalLM.from_pretrained("lgaalves/gpt2-xl_camel-ai-physics")
```

### Training Dataset

`lgaalves/gpt2-xl_camel-ai-physics` trained on the GPT4 generated dataset [lgaalves/camel-physics](https://huggingface.co./datasets/lgaalves/camel-physics).

### Training Procedure

`lgaalves/gpt2-xl_camel-ai-physics` was instruction fine-tuned using LoRA on  1 Tesla V100-SXM2-16GB. It took about 3 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.