Instructions to use baobabtech/evalexplorer-classify-adapters with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use baobabtech/evalexplorer-classify-adapters with PEFT:
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- Notebooks
- Google Colab
- Kaggle
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Download README.md from baobabtech/evalexplorer-classify-adapters: direct link, hf CLI and curl.
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- Download file 3.59 kB
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https://huggingface.co/baobabtech/evalexplorer-classify-adapters/resolve/main/README.md
- Command line
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hf download hf://baobabtech/evalexplorer-classify-adapters/README.md
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curl -L -o README.md https://huggingface.co/baobabtech/evalexplorer-classify-adapters/resolve/main/README.md
3.59 kB
| library_name: peft | |
| license: apache-2.0 | |
| tags: | |
| - evalexplorer | |
| - document-classification | |
| - structured-output | |
| # evalexplorer-classify-adapters | |
| Experimental LoRA adapters for the EvalExplorer document classifier, one subfolder per run. Every adapter here is | |
| on an Apache-2.0 base (Qwen3.5, Gemma 4); each keeps its base model's terms. Adapters worth using | |
| on their own get their own repo (`baobabtech/evalexplorer-classify-<model>-sft`); variants from GRPO and other | |
| trials live here so they do not each need one. Each subfolder holds `adapter_config.json`, | |
| `adapter_model.safetensors`, the tokenizer and `training_log.json`. | |
| | Adapter | Base model | Mean field score | Exact match | Run | | |
| |---|---|---:|---:|---| | |
| | [`qwen3.5-2b-grpo-countries`](https://huggingface.co/baobabtech/evalexplorer-classify-adapters/tree/main/qwen3.5-2b-grpo-countries) | unsloth/Qwen3.5-2B | 0.847 | 0.246 | [report](https://huggingface.co/datasets/baobabtech/evalexplorer-classify-experiments/blob/main/runs/qwen3.5-2b-grpo-countries--test/README.md) | | |
| | [`qwen3.5-2b-grpo-lr5e6`](https://huggingface.co/baobabtech/evalexplorer-classify-adapters/tree/main/qwen3.5-2b-grpo-lr5e6) | unsloth/Qwen3.5-2B | 0.843 | 0.254 | [report](https://huggingface.co/datasets/baobabtech/evalexplorer-classify-experiments/blob/main/runs/qwen3.5-2b-grpo-lr5e6--test/README.md) | | |
| | [`gemma-4-e2b-grpo-lr5e6`](https://huggingface.co/baobabtech/evalexplorer-classify-adapters/tree/main/gemma-4-e2b-grpo-lr5e6) | unsloth/gemma-4-E2B-it | 0.827 | 0.276 | [report](https://huggingface.co/datasets/baobabtech/evalexplorer-classify-experiments/blob/main/runs/gemma-4-e2b-grpo-lr5e6--test/README.md) | | |
| | [`qwen3.5-2b-grpo`](https://huggingface.co/baobabtech/evalexplorer-classify-adapters/tree/main/qwen3.5-2b-grpo) | unsloth/Qwen3.5-2B | 0.822 | 0.179 | [report](https://huggingface.co/datasets/baobabtech/evalexplorer-classify-experiments/blob/main/runs/qwen3.5-2b-grpo--test/README.md) | | |
| | [`gemma-4-e2b-grpo`](https://huggingface.co/baobabtech/evalexplorer-classify-adapters/tree/main/gemma-4-e2b-grpo) | unsloth/gemma-4-E2B-it | 0.819 | 0.157 | [report](https://huggingface.co/datasets/baobabtech/evalexplorer-classify-experiments/blob/main/runs/gemma-4-e2b-grpo--test/README.md) | | |
| | [`gliner2.5-base-passage`](https://huggingface.co/baobabtech/evalexplorer-classify-adapters/tree/main/gliner2.5-base-passage) | fastino/gliner2.5-base-v1 | 0.584 | 0.007 | [report](https://huggingface.co/datasets/baobabtech/evalexplorer-classify-experiments/blob/main/runs/gliner2.5-base-passage--test/README.md) | | |
| | [`gliner2.5-small-passage`](https://huggingface.co/baobabtech/evalexplorer-classify-adapters/tree/main/gliner2.5-small-passage) | fastino/gliner2.5-small-v1 | 0.532 | 0.000 | [report](https://huggingface.co/datasets/baobabtech/evalexplorer-classify-experiments/blob/main/runs/gliner2.5-small-passage--test/README.md) | | |
| Scores are on the 134-document test split of | |
| [`baobabtech/evalexplorer-data`](https://huggingface.co/datasets/baobabtech/evalexplorer-data), config `classify_codes`. Context and every other | |
| run: [`baobabtech/evalexplorer-classify-experiments`](https://huggingface.co/datasets/baobabtech/evalexplorer-classify-experiments). | |
| ```python | |
| from huggingface_hub import snapshot_download | |
| from peft import PeftModel | |
| path = snapshot_download("baobabtech/evalexplorer-classify-adapters", allow_patterns=["gemma-4-e2b-grpo-lr5e6/*"]) | |
| model = PeftModel.from_pretrained(base_model, f"{path}/gemma-4-e2b-grpo-lr5e6") | |
| ``` | |
| New runs can write here with `--output-repo baobabtech/evalexplorer-classify-adapters/<name>`. | |