README.md

---
license: mit
datasets:
- GAIR/lima
language:
- en
pipeline_tag: text-generation
---



# lgaalves/gpt2-xl_lima (1.5B)

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


### Benchmark Metrics

| Metric                |gpt2-xl_lima |gpt2-xl (base) |
|-----------------------|-------|-------|
| Avg.                  | - |  36.66  |
| ARC (25-shot)         | - |  30.29  |
| HellaSwag (10-shot)   | - |  51.38  |
| MMLU (5-shot)         | - |  26.43 |
| TruthfulQA (0-shot)   | - |  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_lima** 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_lima")
>>> 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_lima")
model = AutoModelForCausalLM.from_pretrained("lgaalves/gpt2-xl_lima")
```

### Training Dataset

`lgaalves/gpt2-xl_lima` trained on the [GAIR/lima](https://huggingface.co/datasets/GAIR/lima).

### Training Procedure

`lgaalves/gpt2-xl_lima` was instruction fine-tuned using LoRA on  1 Tesla V100-SXM2-16GB. It took about 10 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.