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--- |
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license: mit |
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datasets: |
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- garage-bAInd/Open-Platypus |
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language: |
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- en |
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pipeline_tag: text-generation |
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--- |
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# GPT-2 Open Platypus |
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**gpt2_open-platypus** is an instruction fine-tuned model based on the GPT-2 transformer architecture. |
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### Benchmark Metrics |
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| Metric |lgaalves/gpt2_open-platypus | gpt2 (base) | |
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|-----------------------|-------|-------| |
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| Avg. | **30.01** | 29.9 | |
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| ARC (25-shot) | **22.18** | 21.84 | |
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| HellaSwag (10-shot) | 31.29 | **31.6** | |
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| MMLU (5-shot) | **26.19** | 25.86 | |
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| TruthfulQA (0-shot) | 40.35 | **40.67** | |
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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. |
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### Model Details |
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* **Trained by**: Luiz G A Alves |
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* **Model type:** **gpt2_open-platypus** is an auto-regressive language model based on the GPT-2 transformer architecture. |
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* **Language(s)**: English |
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### How to use: |
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```python |
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# Use a pipeline as a high-level helper |
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>>> from transformers import pipeline |
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>>> pipe = pipeline("text-generation", model="lgaalves/gpt2_open-platypus") |
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>>> question = "What is a large language model?" |
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>>> answer = pipe(question) |
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>>> print(answer[0]['generated_text']) |
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``` |
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or, you can load the model direclty using: |
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```python |
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# Load model directly |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("lgaalves/gpt2_open-platypus") |
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model = AutoModelForCausalLM.from_pretrained("lgaalves/gpt2_open-platypus") |
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``` |
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### Training Dataset |
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`lgaalves/gpt2_open-platypus` trained using STEM and logic based dataset [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus). |
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### Training Procedure |
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`lgaalves/gpt2_open-platypus` was instruction fine-tuned using LoRA on 1 T4 GPU on Google Colab. It took about 27 minutes to train it. |
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# Intended uses, limitations & biases |
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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. |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lgaalves__gpt2_open-platypus) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 25.16 | |
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| ARC (25-shot) | 22.18 | |
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| HellaSwag (10-shot) | 31.29 | |
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| MMLU (5-shot) | 26.19 | |
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| TruthfulQA (0-shot) | 40.35 | |
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| Winogrande (5-shot) | 51.3 | |
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| GSM8K (5-shot) | 0.15 | |
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| DROP (3-shot) | 4.64 | |
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