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Model: resect-ai/veritas-0.6B-fact-checker-non-thinking-1.0
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---
base_model: Qwen/Qwen3-0.6B
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-0.6B-Fact-Checker-Non-Thinking-1.0
- **Veritas-0.6B-Fact-Checker-Non-Thinking-1.0** is built on the **Qwen3 architecture**, starting from [**(Qwen/Qwen3-0.6B)**](https://huggingface.co/Qwen/Qwen3-0.6B). 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-0.6B-Fact-Checker-Non-Thinking-1.0** achieves an **average score of 72.30%**, an improvement of **7.37%** above Qwen3-0.6B 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 |
|------|------|------|------|------|--------|-------|------|--------|--------------|------------|-----------|------|-----------|
| Qwen3-0.6B (non-thinking) | 0.6B | 64.93 | 57.93 | 66.71 | 57.13 | 62.60 | 67.23 | **86.99** | 59.85 | 72.63 | 56.44 | 70.18 | 56.56 |
| Veritas-0.6B-Fact-Checker-Non-Thinking-1.0 | 0.6B | **72.30** | **65.84** | **66.75** | **68.45** | **73.47** | **73.43** | 83.32 | **71.89** | **73.45** | **58.66** | **82.57** | **77.49** |
<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-0.6B-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-0.6B-fact-checker-non-thinking-1.0', enable_prefix_caching=False, extra_chat_template_kwargs=chat_kwargs, operating_mode="bespoke", max_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) # [0.9796443054985795, 0.008577403129593576]
```
### 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-0.6B-fact-checker-non-thinking-1.0', enable_prefix_caching=False, extra_chat_template_kwargs=chat_kwargs, operating_mode="bespoke", max_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-0.6B-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/).

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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"rope_type": "default"
},
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"tie_word_embeddings": true,
"transformers_version": "5.6.2",
"use_cache": false,
"use_sliding_window": false,
"vocab_size": 151936
}

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{
"bos_token_id": 151643,
"do_sample": true,
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"temperature": 0.6,
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"top_p": 0.95,
"transformers_version": "5.6.2"
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"add_prefix_space": false,
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"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
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"<|video_pad|>"
],
"is_local": false,
"local_files_only": false,
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}