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Model: lgaalves/gpt2-xl_lima
Source: Original Platform

README.md

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+---
+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.                  | 36.65 |  **36.66**  |
+| ARC (25-shot)         | **31.14** |  30.29  |
+| HellaSwag (10-shot)   | 51.28 |  **51.38**  |
+| MMLU (5-shot)         | 25.43 |  **26.43** |
+| TruthfulQA (0-shot)   | **38.74** |  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.
+# [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-xl_lima)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 29.95   |
+| ARC (25-shot)         | 31.14          |
+| HellaSwag (10-shot)   | 51.28    |
+| MMLU (5-shot)         | 25.43         |
+| TruthfulQA (0-shot)   | 38.74   |
+| Winogrande (5-shot)   | 57.22   |
+| GSM8K (5-shot)        | 0.91        |
+| DROP (3-shot)         | 4.89         |