Uploaded model
- Developed by: axel-darmouni
- License: apache-2.0
- Finetuned from model : unsloth/gemma-3-1b-it-unsloth-bnb-4bit
This gemma3_text model was trained 2x faster with Unsloth and Huggingface's TRL library.
This gemma3 model aims to improve gemma-3-1b-it's ability to generate haikus given the prefix "Generate a haiku about the following topic: {topic}". Topics tested were less than a sentence long. Model was trained using the axel-darmouni/haiku_dataset.
Results of all training runs in the training github can be found below:
| Model | Haiku Score | Similarity Score | Total Score | Train Overlap |
|---|---|---|---|---|
| unsloth/gemma-3-1b-it | 0.0372 | -0.0998 | -0.0627 | 0.00% |
| gemma-3-1b-haiku | 0.1351 | 0.1101 | 0.2453 | 0.00% |
| gemma-3-1b-sftrl-haiku | 0.0878 | 0.3708 | 0.4587 | 0.00% |
| gemma-3-1b-sftrl-haiku-sparse | 0.1858 | -0.0880 | 0.0978 | 0.00% |
| gemma-3-haiku-rl-sparse | 0.1537 | -0.1206 | 0.0331 | 0.00% |
| gemma-3-1b-fullrun | 0.2348 | 0.0588 | 0.2936 | 0.00% |
The fullrun which is the model uploaded uses a combination of sft, rl with sparse rewards polished by a run with continuous rewards.
Warning: it is however worth noting the haiku reward might be biased, due to issues with the pyphen library, used to identify haikus.
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Model tree for axel-darmouni/gemma-3-1b-haikuspec
Base model
google/gemma-3-1b-pt Finetuned
google/gemma-3-1b-it Quantized
unsloth/gemma-3-1b-it-unsloth-bnb-4bit