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--- |
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library_name: transformers |
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license: cc-by-nc-4.0 |
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datasets: |
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- oumi-ai/oumi-anli-subset |
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- oumi-ai/oumi-c2d-d2c-subset |
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- oumi-ai/oumi-synthetic-claims |
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- oumi-ai/oumi-synthetic-document-claims |
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language: |
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- en |
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base_model: |
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- meta-llama/Llama-3.1-8B-Instruct |
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--- |
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[](https://github.com/oumi-ai/oumi) |
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[](https://github.com/oumi-ai/oumi) |
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[](https://oumi.ai/docs/en/latest/index.html) |
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[](https://oumi.ai/blog) |
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[](https://discord.gg/oumi) |
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# oumi-ai/HallOumi-8B |
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<!-- Provide a quick summary of what the model is/does. --> |
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Introducing **HallOumi-8B**, a **SOTA hallucination detection model**, outperforming DeepSeek R1, OpenAI o1, Google Gemini 1.5 Pro, and Claude Sonnet 3.5 at only **8 billion parameters!** |
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Give HallOumi a try now! |
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* Demo: https://oumi.ai/halloumi-demo |
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* Github: https://github.com/oumi-ai/oumi/tree/main/configs/projects/halloumi |
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| Model | Macro F1 Score | Open? | Model Size | |
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| --------------------- | -------------- | ----------------- | ---------- | |
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| **HallOumi-8B** | **77.2% ± 2.2%** | Truly Open Source | 8B | |
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| Claude Sonnet 3.5 | 69.6% ± 2.8% | Closed | ?? | |
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| OpenAI o1-preview | 65.9% ± 2.3% | Closed | ?? | |
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| DeepSeek R1 | 61.6% ± 2.5% | Open Weights | 671B | |
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| Llama 3.1 405B | 58.8% ± 2.4% | Open Weights | 405B | |
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| Google Gemini 1.5 Pro | 48.2% ± 1.8% | Closed | ?? | |
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**HallOumi**, the hallucination detection model built with Oumi, is a system built specifically to enable per-sentence verification of any content (either AI or human-generated) with **sentence-level citations** and **human-readable explanations.** |
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For example, when given one or more context documents, as well as an AI-generated summary, HallOumi goes through every claim being made in the summary and identifies: |
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* A determination whether that particular statement is **supported or unsupported** by the provided context combined with a **confidence score**. |
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* The **relevant context sentences** associated with that claim to facilitate human review. |
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* An **explanation** describing why a particular claim is supported or unsupported to boost human review accuracy. Some hallucinations may be nuanced and hard for humans to catch without help. |
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## Hallucinations |
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Hallucinations are often cited as the most important issue with being able to deploy generative models in numerous commercial and personal applications, and for good reason: |
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* [Lawyers sanctioned for briefing where ChatGPT cited 6 fictitious cases](https://www.reuters.com/legal/new-york-lawyers-sanctioned-using-fake-chatgpt-cases-legal-brief-2023-06-22/) |
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* [Air Canada required to honor refund policy made up by its AI support chatbot](https://www.wired.com/story/air-canada-chatbot-refund-policy/) |
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* [AI suggesting users should make glue pizza and eat rocks](https://www.bbc.com/news/articles/cd11gzejgz4o) |
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It ultimately comes down to an issue of **trust** — generative models are trained to produce outputs which are **probabilistically likely**, but not necessarily **true**. |
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While such tools are useful in the right hands, being unable to trust them prevents AI from being adopted more broadly, |
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where it can be utilized safely and responsibly. |
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## Building Trust with Verifiability |
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To be able to begin trusting AI systems, we have to be able to verify their outputs. To verify, we specifically mean that we need to: |
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* Understand the **truthfulness** of a particular statement produced by any model. |
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* Understand what **information supports that statement’s truth** (or lack thereof). |
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* Have **full traceability** connecting the statement to that information. |
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Missing any one of these aspects results in a system that cannot be verified and therefore cannot be trusted. |
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However, this is not enough, as we have to be capable of doing these things in a way that is **meticulous**, **scalable**, and **human-readable**. |
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With explanations, confidence scores, and citations, all at an affordable model size, HallOumi takes us towards a more grounded, trustworthy future for AI. |
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- **Developed by:** [Oumi AI](https://oumi.ai/) |
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- **Model type:** Small Language Model |
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- **Language(s) (NLP):** English |
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- **License:** [CC-BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/deed.en) (due to ANLI data falling under the same license) |
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- **Finetuned from model:** [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) |
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- **Demo:** [HallOumi Demo](https://oumi.ai/halloumi-demo) |
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--- |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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Use to verify claims/detect hallucinations in scenarios where a known source of truth is available. |
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Demo: https://oumi.ai/halloumi-demo |
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Example prompt: |
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``` |
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EXAMPLE_CONTEXT = """<|context|><|s1|><This is sentence 1 of the document.><end||s><|s2|><This is sentence 2 of the document.><end||s><end||context>""" |
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EXAMPLE_REQUEST = """<|request|><Make one or more claims about information in the documents.><end||request>""" |
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EXAMPLE_RESPONSE = """<|response|><|r1|><This is sentence 1 of the claims/response.><end||r><|r2|><This is sentence 2 of the claims/response.><end||r><end||response>""" |
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messages = [ |
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{'role': 'user', 'content': f"{EXAMPLE_CONTEXT}{EXAMPLE_REQUEST}{EXAMPLE_RESPONSE}", |
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] |
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``` |
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## Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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Smaller LLMs have limited capabilities and should be used with caution. Avoid using this model for purposes outside of claim verification. |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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This model was finetuned with Llama-3.1-405B-Instruct data on top of a Llama-3.1-8B-Instruct model, so any biases or risks associated with those models may be present. |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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Training data: |
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- [oumi-ai/oumi-synthetic-document-claims](https://huggingface.co/datasets/oumi-ai/oumi-synthetic-document-claims) |
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- [oumi-ai/oumi-synthetic-claims](https://huggingface.co/datasets/oumi-ai/oumi-synthetic-claims) |
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- [oumi-ai/oumi-anli-subset](https://huggingface.co/datasets/oumi-ai/oumi-anli-subset) |
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- [oumi-ai/oumi-c2d-d2c-subset](https://huggingface.co/datasets/oumi-ai/oumi-c2d-d2c-subset) |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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For information on training, see https://oumi.ai/halloumi |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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Follow along with our notebook on how to evaluate hallucination with HallOumi and other popular models: |
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https://github.com/oumi-ai/oumi/blob/main/configs/projects/halloumi/halloumi_eval_notebook.ipynb |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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- **Hardware Type:** H100 |
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- **Hours used:** 32 (4 * 8 GPUs) |
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- **Cloud Provider:** Google Cloud Platform |
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- **Compute Region:** us-east5 |
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- **Carbon Emitted:** 2.8 kg |
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## Citation |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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``` |
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@misc{oumiHalloumi8B, |
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author = {Jeremy Greer, Konstantinos Aisopos, Panos Achlioptas, Michael Schuler, Oussama Elachqar, Emmanouil Koukoumidis}, |
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title = {HallOumi-8B}, |
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month = {March}, |
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year = {2025}, |
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url = {https://huggingface.co/oumi-ai/HallOumi-8B} |
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} |
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@software{oumi2025, |
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author = {Oumi Community}, |
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title = {Oumi: an Open, End-to-end Platform for Building Large Foundation Models}, |
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month = {January}, |
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year = {2025}, |
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url = {https://github.com/oumi-ai/oumi} |
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} |
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``` |
