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--- |
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license: mit |
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datasets: |
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- GAIR/lima |
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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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# lgaalves/gpt2-xl_lima (1.5B) |
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**lgaalves/lgaalves/gpt2-xl_lima** is an instruction fine-tuned model based on the GPT-2 transformer architecture. |
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### Benchmark Metrics |
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| Metric |gpt2-xl_lima |gpt2-xl (base) | |
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|-----------------------|-------|-------| |
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| Avg. | 36.65 | **36.66** | |
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| ARC (25-shot) | **31.14** | 30.29 | |
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| HellaSwag (10-shot) | 51.28 | **51.38** | |
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| MMLU (5-shot) | 25.43 | **26.43** | |
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| TruthfulQA (0-shot) | **38.74** | 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_lima** 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_lima") |
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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_lima") |
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model = AutoModelForCausalLM.from_pretrained("lgaalves/gpt2-xl_lima") |
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``` |
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### Training Dataset |
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`lgaalves/gpt2-xl_lima` trained on the [GAIR/lima](https://huggingface.co/datasets/GAIR/lima). |
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### Training Procedure |
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`lgaalves/gpt2-xl_lima` was instruction fine-tuned using LoRA on 1 Tesla V100-SXM2-16GB. It took about 10 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-xl_lima) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 29.95 | |
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| ARC (25-shot) | 31.14 | |
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| HellaSwag (10-shot) | 51.28 | |
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| MMLU (5-shot) | 25.43 | |
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| TruthfulQA (0-shot) | 38.74 | |
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| Winogrande (5-shot) | 57.22 | |
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| GSM8K (5-shot) | 0.91 | |
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| DROP (3-shot) | 4.89 | |
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