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Model: lgaalves/gpt2_guanaco-dolly-platypus Source: Original Platform
2026-06-13 13:41:09 +08:00
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+---
+license: mit
+datasets:
+- garage-bAInd/Open-Platypus
+- databricks/databricks-dolly-15k
+- timdettmers/openassistant-guanaco
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# gpt2_guanaco-dolly-platypus
+
+**gpt2_guanaco-dolly-platypus** is an instruction fine-tuned model based on the GPT-2 transformer architecture.
+
+
+### Benchmark Metrics
+
+| Metric                | gpt2_guanaco-dolly-platypus | GPT-2 (base) |
+|-----------------------|-------|-------|
+| Avg.                  | **30.25** | 29.9 |
+| ARC (25-shot)         | **23.55** | 21.84 |
+| HellaSwag (10-shot)   | 31.03 | **31.6** |
+| MMLU (5-shot)         | **26.4** | 25.86 |
+| TruthfulQA (0-shot)   | 40.02 |  **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:**  **gpt2_guanaco-dolly-platypus** 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_guanaco-dolly-platypus")
+>>> 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_open-platypus")
+model = AutoModelForCausalLM.from_pretrained("lgaalves/gpt2_open-platypus")
+```
+
+### Training Dataset
+
+`lgaalves/gpt2_guanaco-dolly-platypus` was trained using 3 datasets:
+ - [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco)
+ - [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)
+ - [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
+
+### Training Procedure
+
+`lgaalves/gpt2_guanaco-dolly-platypus` was instruction fine-tuned using LoRA on 1 T4 GPU on Google Colab. It took about 1 hour 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_guanaco-dolly-platypus)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 25.15   |
+| ARC (25-shot)         | 23.55          |
+| HellaSwag (10-shot)   | 31.03    |
+| MMLU (5-shot)         | 26.4         |
+| TruthfulQA (0-shot)   | 40.02   |
+| Winogrande (5-shot)   | 50.12   |
+| GSM8K (5-shot)        | 0.0        |
+| DROP (3-shot)         | 4.96         |