Create README.md

README.md

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+---
+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.                  | - | 36.66 |
+| 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-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.