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Model: resect-ai/veritas-8B-fact-checker-non-thinking-1.0
Source: Original Platform

.gitattributes

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README.md

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+---
+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/).

added_tokens.json

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+{
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+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
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+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
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+  "<|fim_suffix|>": 151661,
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+  "<|object_ref_end|>": 151647,
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+  "<|quad_end|>": 151651,
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chat_template.jinja

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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 %}

config.json

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+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

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+  "do_sample": true,
+  "eos_token_id": [
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+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.52.0"
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