---
base_model: Qwen/Qwen3-8B
tags:
- transformers
- factual-grounding
- fact-checking
- qwen3
license: apache-2.0
language:
- en
---
# About Resect Research Labs
- Resect Research Labs focuses on improving factual grounding as well as detecting, reducing, and mitigating hallucinations in AI models through proprietary reinforcement learning and novel fine-tuning techniques.

## Introducing: Veritas-8B-Fact-Checker-Non-Thinking-1.0
- **Veritas-8B-Fact-Checker-Non-Thinking-1.0** is built on the **Qwen3 architecture**, starting from [**(Qwen/Qwen3-8B)**](https://huggingface.co/Qwen/Qwen3-8B). Resect Research Labs has specialized, finetuned, and optimized this model for **fact-checking and factual consistency verification**.

### Model Performance
- The performance of this model is evaluated on [LLM-AggreFact](https://huggingface.co/datasets/lytang/LLM-AggreFact) (unseen by this model during training),
the benchmark is an aggregation of 11 human annotated datasets on fact-checking and grounding. 

### Overall Performance
- **Veritas-8B-Fact-Checker-Non-Thinking-1.0** achieves an **average score of 75.47%**, an improvement of **2.3%** above Qwen3-8B in non-thinking mode.

### Benchmark Details (LLM-AggreFact)

Balanced Accuracy Scores

| Model | Size | Avg | CNN | XSum | MediaS | MeetB | WiCE | REVEAL | Claim Verify | Fact Check | Expert QA | LFQA | RAG Truth |
|------|------|------|------|------|--------|-------|------|--------|--------------|------------|-----------|------|-----------|
| Qwen3-8B (non-thinking) | 8B | 73.17 | 66.30 | 71.25 | 69.05 | 77.50 | 76.16 | 84.81 | 67.71 | **77.39** | 57.14 | 80.62 | 76.91 | 
| Veritas-8B-Fact-Checker-Non-Thinking-1.0 | 8B | **75.47** | **68.84** | **74.33** | **71.83** | **76.44** | **79.06** | **86.94** | **72.40** | 76.75 | **58.12** | **84.14** | **81.30** |


<sup>**The benchmarks noted here for Veritas-0.6B-Fact-Checker-Non-Thinking-1.0 were performed on the test set and a PR has been submitted to [Minicheck's Library (Pull Request)](https://github.com/Liyan06/MiniCheck/pull/17) to support additional operating modes including this model.**</sup>
<sup>Note: Performance may vary slightly depending on hardware configuration and vLLM version</sup>

---

# Model Usage 

## Scope of Use

* Veritas-8B-Fact-Checker-Non-Thinking model must only be used strictly for the prescribed scoring mode, which generates a binary classification based on the specified template. Any deviation from this intended use may lead to unexpected outputs.

## Using Minicheck's library [^2]

**Requires the changes from our Pull Request to be merged, see [^2]**

Please run the following command to install the **MiniCheck package** and all necessary dependencies.
```sh
pip install "minicheck[llm] @ git+https://github.com/Liyan06/MiniCheck.git@main"
```

[^2]: Pull Request to [Minicheck's library submitted](https://github.com/Liyan06/MiniCheck/pull/17) awaiting review

#### Below is a simple use case

```python
from minicheck.minicheck import MiniCheck
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"

doc = "A group of students gather in the school library to study for their upcoming final exams."
claim_1 = "The students are preparing for an examination."
claim_2 = "The students are on vacation."

chat_kwargs = {'enable_thinking': False}

scorer = MiniCheck(model_name='resect-ai/veritas-8B-fact-checker-non-thinking-1.0', enable_prefix_caching=False, extra_chat_template_kwargs=chat_kwargs, operating_mode="bespoke", max_new_tokens=1, cache_dir='./ckpts', bypass_model_check=True)
pred_label, raw_prob, _, _ = scorer.score(docs=[doc, doc], claims=[claim_1, claim_2]) # can set `chunk_size=your-specified-value` here, default to 32K chunk size. 

print(pred_label) # [1, 0]
print(raw_prob)   # [0.9465315396494047, 0.008577206810662688]
```

### Test on [LLM-AggreFact](https://huggingface.co/datasets/lytang/LLM-AggreFact) Benchmark [^2]

```python
import pandas as pd
from datasets import load_dataset
from minicheck.minicheck import MiniCheck
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"

# load 30K test data
df = pd.DataFrame(load_dataset("lytang/LLM-AggreFact")['test'])
docs = df.doc.values
claims = df.claim.values

chat_kwargs = {'enable_thinking': False}

scorer = MiniCheck(model_name='resect-ai/veritas-8B-fact-checker-non-thinking-1.0', enable_prefix_caching=False, extra_chat_template_kwargs=chat_kwargs, operating_mode="bespoke", max_new_tokens=1, cache_dir='./ckpts', bypass_model_check=True)
pred_label, raw_prob, _, _ = scorer.score(docs=docs, claims=claims)
```

To evaluate the result on the benchmark
```python
from sklearn.metrics import balanced_accuracy_score

df['preds'] = pred_label
result_df = pd.DataFrame(columns=['Dataset', 'BAcc'])
for dataset in df.dataset.unique():
    sub_df = df[df.dataset == dataset]
    bacc = balanced_accuracy_score(sub_df.label, sub_df.preds) * 100
    result_df.loc[len(result_df)] = [dataset, bacc]

result_df.loc[len(result_df)] = ['Average', result_df.BAcc.mean()]
result_df.round(1)
```

[^2]: Pull Request to [Minicheck's library submitted](https://github.com/Liyan06/MiniCheck/pull/17) awaiting review

# License
- This model **Veritas-8B-Fact-Checker-Non-Thinking-1.0** is bound by the Apache 2.0 license found at https://choosealicense.com/licenses/apache-2.0. By downloading and using this model you agree to the license terms.


# Acknowledgements

Model perfected by [Resect Research Labs](https://www.resect.ai/).