A finetuned model designed to recognize and classify Personally Identifiable Information (PII) within unstructured text data. This powerful model accurately identifies a wide range of PII categories, such as account names, credit card numbers, emails, phone numbers, and addresses. The model is specifically trained to detect various PII types, including but not limited to: The PII Identifier Model ensures data privacy and compliance by effectively detecting and categorizing sensitive information within documents, emails, user-generated content, and more. Make your data processing safer and more secure with our state-of-the-art PII detection technology.
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View details ↗h2o-danube3-500m-chat is a chat fine-tuned model by H2O.ai with 500 million parameters. We release two versions of this model: This model was trained using H2O LLM Studio. Can be run natively and fully offline on phones - try it yourself with H2O AI Personal GPT.
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View details ↗H2O-Lightning-4B is a 4B-parameter decision model from H2O.ai, built on Qwen/Qwen3.5-4B. It answers typed decision questions about a record (a document, a ticket, a policy, a conversation) and about images that come with the record (photos, screenshots, scanned documents, charts). It returns a probability for every option: **Try it in your browser:** live demo on Hugging Face Spaces (text and images; built by the Hugging Face team and running on ZeroGPU). **Other formats:** GGUF for llama.cpp: h2oai/h2o-lightning-4b-gguf. MLX for Apple silicon, built by NetProjects: netprojects/h2o-lightning-4b-mlx-q8 (8-bit; two-way and one-way) and netprojects/h2o-lightning-4b-mlx-q4 (4-bit; one-way only). They carry this release's weights and serving files; NetProjects reports 205 / 231 on JevBench's public items two-way and 201 one-way for the 8-bit build. A variant on a different base: h2oai/h2o-lightning-4b-nemotron-nano, built on NVIDIA Nemotron 3 Nano 4B; text only; NVIDIA Nemotron Open Model License.
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View details ↗GGUF files of H2O-Lightning-4B for llama.cpp. For the model description, training and general benchmarks, see the model page. Requests with images use a temperature of 1.0, set in the native files' metadata (the `stock-` path reads it from the shim's configuration). The model answers typed decisions with probabilities: named `choice` options, true/false `noul`, ordered `score` levels, and `box` (a bounding box in an image).
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View details ↗h2oGPT clone of Meta's Llama 2 70B Chat. Try it live on our h2oGPT demo with side-by-side LLM comparisons and private document chat! See how it compares to other models on our LLM Leaderboard!
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View details ↗H2O.ai's `h2ogpt-oig-oasst1-512-6_9b` is a 6.9 billion parameter instruction-following large language model licensed for commercial use. To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed. Alternatively, if you prefer to not use `trust_remote_code=True` you can download instruct_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:
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