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--- |
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license: apple-amlr |
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base_model: |
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- mistralai/Mistral-7B-Instruct-v0.2 |
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tags: |
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- rag |
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- compression |
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- retrieval |
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- generation |
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--- |
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# CLaRa-7B-Base (Compression-16 & 128) |
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The CLaRa-7B-Base model is our foundational unified RAG model with built-in semantic document compression (16× and 128x). |
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It provides a base compressor + generator capable of producing answers directly from compressed document representations. |
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**Training recipe:** Trained using QA-guided semantic compression and paraphrase consistency objectives. |
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**Benchmarks:** Strong baseline performance across multi-hop QA tasks under a 16× compression ratio. |
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--- |
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## More details and usage examples: |
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Paper: [CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning](https://arxiv.org/abs/2511.18659) |
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GitHub: https://github.com/apple/ml-clara |
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--- |
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## Example Usage |
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```python |
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from transformers import AutoModel |
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unirag = AutoModel.from_pretrained( |
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"/mnt/ceph_rbd/model/CLaRa-7B-Base/compression-16", |
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trust_remote_code=True |
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).to("cuda") |
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documents = [ |
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[ |
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"Weldenia is a monotypic genus of flowering plant in the family Commelinaceae...", |
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"Hagsatera is a genus of orchids native to Mexico and Guatemala...", |
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"Alsobia is a genus of flowering plants native to Mexico and Central America..." |
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] |
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] |
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questions = [""] |
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out = unirag.generate_from_paraphrase( |
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questions=questions, |
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documents=documents, |
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max_new_tokens=64 |
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) |
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print("Generated answer:", out) |
