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Model: Vedant3907/gpt2-irish-folk-tune-generator Source: Original Platform
2026-06-26 17:21:18 +08:00
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+---
+language:
+- en
+license: mit
+library_name: transformers
+base_model: openai-community/gpt2
+datasets:
+- sander-wood/irishman
+tags:
+- text-generation
+- music-generation
+- abc-notation
+- irish-music
+- gpt2
+- full-finetune
+pipeline_tag: text-generation
+---
+
+Try the live demo Space: https://huggingface.co/spaces/Vedant3907/gpt2-irish-folk-tune-generator-space
+
+
+<video controls width="100%">
+  <source src="https://huggingface.co/Vedant3907/gpt2-irish-folk-tune-generator/resolve/main/assets/irish-gpt2-model.mp4" type="video/mp4">
+</video>
+
+Weekend project demo: an original dataset tune is played first, followed by a GPT-2 generated tune from similar ABC notation context.
+
+
+# GPT-2 Irish ABC Tune Generator
+
+This model is a full fine-tune of `openai-community/gpt2` on the
+`sander-wood/irishman` dataset, a collection of Irish folk tunes represented in
+ABC notation.
+
+The model generates symbolic music text, not audio. Generated output can be
+pasted into an ABC player such as [abc.rectanglered.com](https://abc.rectanglered.com/)
+or [ABCjs Editor](https://editor.drawthedots.com/) to hear the tune.
+
+## Model Details
+
+- **Base model:** `openai-community/gpt2`
+- **Training method:** Full fine-tuning
+- **Dataset:** `sander-wood/irishman`
+- **Task:** Causal language modeling / ABC notation continuation
+- **Max sequence length:** 512 tokens
+- **Training hardware:** Google Colab T4 GPU
+- **Training duration:** Approximately one epoch
+- **Validation loss:** `0.9962592720985413`
+
+## What The Model Learns
+
+The training text was formatted as:
+
+```text
+<control code>
+<ABC notation>
+```
+
+So a prompt can start with a control code, and the model will continue by
+generating ABC notation.
+
+Example prompt:
+
+```text
+S:2 B:8 E:6 B:8
+```
+
+The output should look like ABC music notation with headers and note sequences.
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "Vedant3907/gpt2-irish-folk-tune-generator"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+model.eval()
+
+def generate_tune(prompt="S:2 B:8 E:6 B:8\n", max_new_tokens=400):
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    with torch.no_grad():
+        output = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            do_sample=True,
+            temperature=0.9,
+            top_k=40,
+            top_p=0.95,
+            pad_token_id=tokenizer.eos_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+        )
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+print(generate_tune())
+```
+
+## Example Prompt
+
+```text
+S:2 B:8 E:6 B:8
+```
+
+If the generated output does not include a complete ABC header, add one manually
+before playing it:
+
+```text
+X:1
+M:4/4
+L:1/8
+K:G
+```
+
+Then paste the full ABC text into:
+
+- <https://abc.rectanglered.com/>
+- <https://editor.drawthedots.com/>
+- <https://thesession.org/tunes/convert>
+
+## Evaluation
+
+The model was evaluated on the dataset validation split.
+
+```text
+eval_loss: 0.9962592720985413
+```
+
+The final logged training loss was around `1.02`, and the validation loss was
+close to that value, suggesting the model did not obviously overfit during this
+run.
+
+
+
+## Training Loss Table
+
+Compact view of the training loss curve. The full logged loss table is available in `training_loss_curve.csv`.
+
+| Step | Training Loss |
+|---:|---:|
+| 50 | 3.143394 |
+| 500 | 1.461160 |
+| 1000 | 1.315162 |
+| 1500 | 1.255494 |
+| 2000 | 1.233580 |
+| 2500 | 1.160884 |
+| 3000 | 1.140212 |
+| 3500 | 1.128867 |
+| 4000 | 1.115015 |
+| 4500 | 1.089873 |
+| 5000 | 1.078019 |
+| 5500 | 1.059447 |
+| 6000 | 1.082058 |
+| 6500 | 1.072979 |
+| 7000 | 1.060261 |
+| 7500 | 1.066326 |
+| 8000 | 1.051891 |
+| 8500 | 1.054574 |
+| 9000 | 1.058629 |
+| 9500 | 1.041122 |
+| 10000 | 1.046246 |
+| 10500 | 1.023033 |
+| 11000 | 1.031550 |
+| 11500 | 1.030448 |
+| 12000 | 1.031115 |
+| 12500 | 1.029919 |
+| 12800 | 1.029599 |
+
+## Limitations
+
+- The model generates ABC notation, not direct audio.
+- Some generations may be syntactically invalid ABC.
+- Some outputs may need manual cleanup before playback.
+- The model may reproduce fragments or patterns from the training data.
+- Musical quality varies; sampling multiple outputs and selecting the best one
+is recommended.
+
+## Intended Use
+
+This model is intended for experimentation with Irish folk tune generation,
+symbolic music modeling, and ABC notation text generation.
+
+It is not intended for claims of originality or commercial music production
+without additional review for memorization and licensing concerns.
+
+## Training Summary
+
+This was trained as a practical free-GPU fine-tuning experiment after the
+original Karpathy `autoresearch` training setup proved unsuitable for Google
+Colab's free T4 GPU. Instead of training from scratch, this model uses GPT-2 as
+a pretrained base and adapts it to Irish ABC notation.