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--- |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- code |
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- fact |
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- math |
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- short-context-reasoning |
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license: apache-2.0 |
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language: |
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- en |
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- zh |
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base_model: |
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- Qwen/Qwen2.5-0.5B-Instruct |
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pipeline_tag: text-generation |
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--- |
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# **TESS-QwenRe-Fact-0.5B** |
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> **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**. |
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## **Key Features** |
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1. **Fact Verification & Correction** |
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Trained to analyze factual accuracy in statements and offer corrected or clarified responses, making it ideal for real-time verification tasks and misinformation mitigation. |
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2. **Concise Reasoning** |
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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. |
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3. **Multilingual Support (EN + ZH)** |
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Supports fact-checking tasks in both **English** and **Simplified Chinese**, enhancing accessibility for bilingual or regional use cases. |
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4. **Built on Qwen2.5 0.5B** |
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Combines the latest architectural improvements from **Qwen2.5** with a small parameter footprint (0.5B), optimized for **speed**, **efficiency**, and **edge-device compatibility**. |
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5. **Prompt-Friendly Output** |
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Responds well to well-structured queries, returning clean, interpretable answers — especially for true/false classification, source-based fact validation, and yes/no reasoning. |
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## **Quickstart with Transformers** |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/TESS-QwenRe-Fact-0.5B" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "Is the capital of Australia Sydney? Explain briefly." |
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messages = [ |
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{"role": "system", "content": "You are a concise and accurate fact-checking assistant."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=256 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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``` |
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## **Intended Use** |
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- **Fact-Checking Assistants**: Quickly verify factual claims in conversation or content. |
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- **Digital Truth Detectors**: Misinformation and rumor detection in social feeds or news summaries. |
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- **Micro-Reasoning Bots**: Smart agents for short-form logic and rationale generation. |
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- **Multilingual Knowledge Tools**: Fact reasoning in **EN/ZH**, ideal for diverse platforms. |
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## **Limitations** |
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1. **Limited Depth** |
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Focused on **short-form reasoning** — may not perform well on multi-step or abstract logic tasks. |
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2. **Compact Model Scale** |
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At 0.5B parameters, it prioritizes **efficiency over complexity** — best for straightforward fact-based tasks. |
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3. **Language & Topic Bias** |
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Inherits limitations and biases from its base model Qwen2.5 0.5B. Use carefully in sensitive contexts. |
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4. **Prompt Clarity Required** |
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Structured prompts result in higher factual accuracy and shorter response latency. |
