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--- |
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license: mit |
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datasets: |
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- lgaalves/camel-ai-physics |
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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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# gpt2-xl-camel-ai-physics (1.5B) |
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**lgaalves/gpt2-xl_camel-ai-physics** 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-xl_camel-ai-physics |gpt2-xl (base) | |
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|-----------------------|-------|-------| |
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| Avg. | 36.51 | **36.66** | |
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| ARC (25-shot) | 29.52 | **30.29** | |
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| HellaSwag (10-shot) | 50.62 | **51.38** | |
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| MMLU (5-shot) | **26.79** | 26.43 | |
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| TruthfulQA (0-shot) | **39.12** | 38.54 | |
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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:** **lgaalves/gpt2-xl_camel-ai-physics** 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-xl_camel-ai-physics") |
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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-xl_camel-ai-physics") |
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model = AutoModelForCausalLM.from_pretrained("lgaalves/gpt2-xl_camel-ai-physics") |
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``` |
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### Training Dataset |
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`lgaalves/gpt2-xl_camel-ai-physics` trained on the GPT4 generated dataset [lgaalves/camel-physics](https://huggingface.co./datasets/lgaalves/camel-physics). |
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### Training Procedure |
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`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. |
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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__gpt-2-xl_camel-ai-physics) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 29.9 | |
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| ARC (25-shot) | 29.52 | |
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| HellaSwag (10-shot) | 50.62 | |
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| MMLU (5-shot) | 26.79 | |
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| TruthfulQA (0-shot) | 39.12 | |
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| Winogrande (5-shot) | 57.54 | |
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| GSM8K (5-shot) | 0.15 | |
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| DROP (3-shot) | 5.57 | |
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