---
library_name: transformers
tags:
- text-generation-inference
- code
- fact
- math
- short-context-reasoning
license: apache-2.0
language:
- en
- zh
base_model:
- Qwen/Qwen2.5-0.5B-Instruct
pipeline_tag: text-generation
---
![5.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/UWb3145w-rFhJLCytbsBV.png)

# **TESS-QwenRe-Fact-0.5B**

> **TESS-QwenRe-Fact-0.5B** is a **compact fact-checking and short reasoning model** built upon **Qwen2.5 0.5B**. Designed for rapid response, real-world fact verification, and concise logical reasoning, this lightweight model is ideal for digital assistants, quick-response tools, and misinformation detection systems in **English** and **Chinese**.

## **Key Features**

1. **Fact Verification & Correction**  
   Trained to analyze factual accuracy in statements and offer corrected or clarified responses, making it ideal for real-time verification tasks and misinformation mitigation.

2. **Concise Reasoning**  
   Specializes in **short-form reasoning**, capable of analyzing and explaining claims, decisions, or statements in just a few logical steps — perfect for Q&A bots and assistant systems.

3. **Multilingual Support (EN + ZH)**  
   Supports fact-checking tasks in both **English** and **Simplified Chinese**, enhancing accessibility for bilingual or regional use cases.

4. **Built on Qwen2.5 0.5B**  
   Combines the latest architectural improvements from **Qwen2.5** with a small parameter footprint (0.5B), optimized for **speed**, **efficiency**, and **edge-device compatibility**.

5. **Prompt-Friendly Output**  
   Responds well to well-structured queries, returning clean, interpretable answers — especially for true/false classification, source-based fact validation, and yes/no reasoning.

## **Quickstart with Transformers**

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/TESS-QwenRe-Fact-0.5B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Is the capital of Australia Sydney? Explain briefly."
messages = [
    {"role": "system", "content": "You are a concise and accurate fact-checking assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=256
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```

## **Intended Use**

- **Fact-Checking Assistants**: Quickly verify factual claims in conversation or content.
- **Digital Truth Detectors**: Misinformation and rumor detection in social feeds or news summaries.
- **Micro-Reasoning Bots**: Smart agents for short-form logic and rationale generation.
- **Multilingual Knowledge Tools**: Fact reasoning in **EN/ZH**, ideal for diverse platforms.

## **Limitations**

1. **Limited Depth**  
   Focused on **short-form reasoning** — may not perform well on multi-step or abstract logic tasks.

2. **Compact Model Scale**  
   At 0.5B parameters, it prioritizes **efficiency over complexity** — best for straightforward fact-based tasks.

3. **Language & Topic Bias**  
   Inherits limitations and biases from its base model Qwen2.5 0.5B. Use carefully in sensitive contexts.

4. **Prompt Clarity Required**  
   Structured prompts result in higher factual accuracy and shorter response latency.