README.md

---
library_name: transformers
license: cc-by-nc-4.0
datasets:
- oumi-ai/oumi-anli-subset
- oumi-ai/oumi-c2d-d2c-subset
- oumi-ai/oumi-synthetic-claims
- oumi-ai/oumi-synthetic-document-claims
language:
- en
base_model:
- meta-llama/Llama-3.1-8B-Instruct
---

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# oumi-ai/HallOumi-8B

<!-- Provide a quick summary of what the model is/does. -->

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!**

Give HallOumi a try now!

* Demo: https://oumi.ai/halloumi-demo
* Github: https://github.com/oumi-ai/oumi/tree/main/configs/projects/halloumi

| Model                 | Macro F1 Score | Open?             | Model Size |
| --------------------- | -------------- | ----------------- | ---------- |
| **HallOumi-8B**       | **77.2% ± 2.2%** | Truly Open Source | 8B       |
| Claude Sonnet 3.5     | 69.6% ± 2.8%   | Closed            | ??         |
| OpenAI o1-preview     | 65.9% ± 2.3%   | Closed            | ??         |
| DeepSeek R1           | 61.6% ± 2.5%   | Open Weights      | 671B       |
| Llama 3.1 405B        | 58.8% ± 2.4%   | Open Weights      | 405B       |
| Google Gemini 1.5 Pro | 48.2% ± 1.8%   | Closed            | ??         |

**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.** 
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:
* A determination whether that particular statement is **supported or unsupported** by the provided context combined with a **confidence score**.
* The **relevant context sentences** associated with that claim to facilitate human review.
* 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.

## Hallucinations
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:

* [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/)
* [Air Canada required to honor refund policy made up by its AI support chatbot](https://www.wired.com/story/air-canada-chatbot-refund-policy/)
* [AI suggesting users should make glue pizza and eat rocks](https://www.bbc.com/news/articles/cd11gzejgz4o)

It ultimately comes down to an issue of **trust** — generative models are trained to produce outputs which are **probabilistically likely**, but not necessarily **true**. 
While such tools are useful in the right hands, being unable to trust them prevents AI from being adopted more broadly, 
where it can be utilized safely and responsibly.

## Building Trust with Verifiability
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:

* Understand the **truthfulness** of a particular statement produced by any model.
* Understand what **information supports that statement’s truth** (or lack thereof).
* Have **full traceability** connecting the statement to that information.

Missing any one of these aspects results in a system that cannot be verified and therefore cannot be trusted.
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**.
With explanations, confidence scores, and citations, all at an affordable model size, HallOumi takes us towards a more grounded, trustworthy future for AI.

- **Developed by:** [Oumi AI](https://oumi.ai/)
- **Model type:** Small Language Model
- **Language(s) (NLP):** English
- **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)
- **Finetuned from model:** [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)
- **Demo:** [HallOumi Demo](https://oumi.ai/halloumi-demo)

---

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
Use to verify claims/detect hallucinations in scenarios where a known source of truth is available.

Demo: https://oumi.ai/halloumi-demo

Example prompt:
```
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>"""
EXAMPLE_REQUEST = """<|request|><Make one or more claims about information in the documents.><end||request>"""
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>"""

messages = [
	{'role': 'user', 'content': f"{EXAMPLE_CONTEXT}{EXAMPLE_REQUEST}{EXAMPLE_RESPONSE}",
]
```

## Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
Smaller LLMs have limited capabilities and should be used with caution. Avoid using this model for purposes outside of claim verification.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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.

## Training Details

### Training Data

<!-- 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. -->
Training data: 
- [oumi-ai/oumi-synthetic-document-claims](https://huggingface.co/datasets/oumi-ai/oumi-synthetic-document-claims)
- [oumi-ai/oumi-synthetic-claims](https://huggingface.co/datasets/oumi-ai/oumi-synthetic-claims)
- [oumi-ai/oumi-anli-subset](https://huggingface.co/datasets/oumi-ai/oumi-anli-subset)
- [oumi-ai/oumi-c2d-d2c-subset](https://huggingface.co/datasets/oumi-ai/oumi-c2d-d2c-subset)

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
For information on training, see https://oumi.ai/halloumi

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->
Follow along with our notebook on how to evaluate hallucination with HallOumi and other popular models:

https://github.com/oumi-ai/oumi/blob/main/configs/projects/halloumi/halloumi_eval_notebook.ipynb

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

- **Hardware Type:** H100
- **Hours used:** 32 (4 * 8 GPUs)
- **Cloud Provider:** Google Cloud Platform
- **Compute Region:** us-east5
- **Carbon Emitted:** 2.8 kg 

## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

```
@misc{oumiHalloumi8B,
  author = {Jeremy Greer, Konstantinos Aisopos, Panos Achlioptas, Michael Schuler, Oussama Elachqar, Emmanouil Koukoumidis},
  title = {HallOumi-8B},
  month = {March},
  year = {2025},
  url = {https://huggingface.co/oumi-ai/HallOumi-8B}
}

@software{oumi2025,
  author = {Oumi Community},
  title = {Oumi: an Open, End-to-end Platform for Building Large Foundation Models},
  month = {January},
  year = {2025},
  url = {https://github.com/oumi-ai/oumi}
}
```