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Model: prithivMLmods/TESS-QwenRe-Fact-0.5B
Source: Original Platform

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README.md

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+---
+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.

added_tokens.json

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config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 896,
+  "initializer_range": 0.02,
+  "intermediate_size": 4864,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 14,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.50.0.dev0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.50.0.dev0"
+}

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special_tokens_map.json

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tokenizer_config.json

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+  "clean_up_tokenization_spaces": false,
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+  "errors": "replace",
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+  "pad_token": "<|endoftext|>",
+  "padding_side": "right",
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+  "tokenizer_class": "Qwen2Tokenizer",
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+}