Update README.md

README.md

@@ -1,199 +1,147 @@
 ---
 library_name: transformers
-tags: []
+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
 ---
 
-# Model Card for Model ID
+# oumi-ai/HallOumi-8B-classifier
 
 <!-- Provide a quick summary of what the model is/does. -->
 
+Introducing **HallOumi-8B-classifier**, a **SOTA hallucination detection model**, outperforming DeepSeek R1, OpenAI o1, Google Gemini 1.5 Pro, and Anthropic Sonnet 3.5 at only **8 billion parameters!**
 
+Give HallOumi a try now!
 
-## Model Details
+* Demo: https://oumi.ai/halloumi
+* Github: TODO
 
-### Model Description
+| Model                 | Balanced Accuracy | Macro F1 Score | Open Source? | Model Size |
+| --------------------- | ----------------- | --------------------------------------- | ------------ | ---------- |
+| **HallOumi-8B**       | **73.0% ± 2.2%**  | **75.1% ± 2.2%**                        | ✔️           | 8B         |
+| Anthropic Sonnet 3.5  | 67.3% ± 2.7%      | 69.6% ± 2.8%                            | ❌            | ??         |
+| OpenAI o1-preview     | 64.5% ± 2.0%      | 65.9% ± 2.3%                            | ❌            | ??         |
+| DeepSeek R1           | 60.7% ± 2.1%      | 61.6% ± 2.5%                            | ✔️           | 671B       |
+| Llama 3.1 405B        | 58.7% ± 1.7%      | 58.8% ± 2.4%                            | ✔️           | 405B       |
+| Google Gemini 1.5 Pro | 52.9% ± 1.0%      | 48.2% ± 1.8%                            | ❌            | ??         |
 
-<!-- Provide a longer summary of what this model is. -->
+Demo GIF: TODO
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+**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:
+* The **relevant context sentences** associated with that claim.
+* A determination whether that particular statement is **supported or unsupported** by the provided context.
+* An **explanation** describing why a particular claim is supported or unsupported.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+**HallOumi-8B-classifier** is trained with similar data to HallOumi-8B but is instead trained as a classifier rather than a generative model.
+* ✔️ Fast
+* ✔️ Per-claim support (must call once per claim)
+* ❌ No Explanations
+* ❌ No Citations
 
-### Model Sources [optional]
+## 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:
 
-<!-- Provide the basic links for the model. -->
+* [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)
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+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 certainly 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.
 
-## Uses
+## 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:
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+* 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.
 
-### Direct Use
+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**.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+- **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)
+- **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)
 
-[More Information Needed]
+---
 
-### Downstream Use [optional]
+## Uses
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+<!-- 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.
 
-[More Information Needed]
+Demo: https://oumi.ai/halloumi
 
-### Out-of-Scope Use
+## Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+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. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+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. -->
-
-[More Information Needed]
+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. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+Training notebook: Coming Soon
 
 ## Evaluation
 
 <!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+Eval notebook: Coming Soon
 
 ## Environmental Impact
 
 <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
+- **Hardware Type:** H100
+- **Hours used:** 32 (4 * 8 GPUs)
+- **Cloud Provider:** Google Cloud Platform
+- **Compute Region:** us-east5
+- **Carbon Emitted:** 2.8 kg 
 
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
+## Citation
 
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+```
+@misc{oumiHalloumi8BClassifier,
+  author = {Jeremiah Greer},
+  title = {HallOumi-8B-classifier},
+  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}
+}
+```