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Model: lgaalves/gpt2-dolly Source: Original Platform
2026-07-20 21:10:11 +08:00
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+---
+license: mit
+datasets:
+- databricks/databricks-dolly-15k
+language:
+- en
+pipeline_tag: text-generation
+---
+
+
+
+# GPT-2-dolly
+
+**GPT-2-dolly** is an instruction fine-tuned model based on the GPT-2 transformer architecture.
+
+
+### Benchmark Metrics
+
+| Metric                | GPT-2-dolly | GPT-2 (base) |
+|-----------------------|-------|-------|
+| Avg.                  | **30.91** | 29.99 |
+| ARC (25-shot)         | **22.70** | 21.84 |
+| HellaSwag (10-shot)   | 30.15 | **31.6** |
+| MMLU (5-shot)         | 25.81 | **25.86** |
+| TruthfulQA (0-shot)   | **44.97** | 40.67 |
+
+
+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:**  **GPT-2-dolly** 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-dolly")
+>>> 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-dolly")
+model = AutoModelForCausalLM.from_pretrained("lgaalves/gpt2-dolly")
+```
+
+### Training Dataset
+
+`lgaalves/gpt2-dolly` trained using the Databricks Dolly dataset [`databricks/databricks-dolly-15k`](https://huggingface.co/datasets/databricks/databricks-dolly-15k).
+
+
+### Training Procedure
+
+`lgaalves/gpt2-dolly` was instruction fine-tuned using LoRA on 1 T4 GPU on Google Colab. It took about 1.5 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.
+
+
+# [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-dolly)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 25.53   |
+| ARC (25-shot)         | 22.7          |
+| HellaSwag (10-shot)   | 30.15    |
+| MMLU (5-shot)         | 25.81         |
+| TruthfulQA (0-shot)   | 44.97   |
+| Winogrande (5-shot)   | 51.46   |
+| GSM8K (5-shot)        | 0.15        |
+| DROP (3-shot)         | 3.45         |