初始化项目，由ModelHub XC社区提供模型

Model: yugbirla/toxsense-json-ultimate
Source: Original Platform

README.md

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+---
+library_name: transformers
+license: mit
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+- FacebookAI/roberta-base
+---
+# Model Card for ToxSense (Multimodal Adversarial Safety Moderator)
+
+## Model Details
+
+### Model Description
+ToxSense (formerly ModGuard) is a highly precise, multimodal AI safety moderator designed to detect complex, zero-shot adversarial hate speech, sarcasm, and benign confounders. Unlike standard binary classifiers, ToxSense uses a Chain-of-Thought (CoT) reasoning approach across both Image and Text modalities, outputting structured JSON to categorize content into granular safety taxonomies.
+
+* **Developed by:** Yug Birla
+* **Model type:** Causal Language Model (Fine-tuned for strict JSON-In/JSON-Out Multimodal Reasoning)
+* **Language(s) (NLP):** English
+* **License:** MIT
+* **Finetuned from model:** Open-source Qwen Base Architecture
+
+### Model Sources
+* **Demo:** [ToxSense Safety Center Dashboard](https://huggingface.co/spaces/yugbirla/ToxSense-Safety-Center)
+
+### Model Architecture and Objective
+* **Text Engine:** Qwen base + LoRA adapters (Merged)
+* **Vision Dependency:** Designed to ingest outputs from Salesforce BLIP and EasyOCR.
+* **Optimization:** DPO + SFT
+* 
+## Uses
+
+### Direct Use
+* API-ready content moderation for detecting nuanced hate speech in memes and multimodal posts.
+* Multi-category classification (e.g., Harassment, Racism, Threat, Insult, Sexism).
+* Generating transparent "Chain-of-Thought" reasoning for why a post was flagged.
+
+### Downstream Use
+* Integration into Trust & Safety dashboards for social media platforms.
+* Assisting human moderators by pre-filtering and providing contextual explanations for flagged content.
+
+### Out-of-Scope Use
+* Fully automated, zero-human-in-the-loop bans for highly ambiguous cases. 
+* Medical, legal, or strictly unimodal text classification without proper prompt formatting.
+
+## Bias, Risks, and Limitations
+**The "Safety Tax" / Safe-Bias:** Due to the base RLHF alignment and subsequent Direct Preference Optimization (DPO), ToxSense exhibits a strong "safe-bias." It requires a very high threshold of proof to classify content as hateful. While this lowers the overall recall, it was an intentional product design choice to achieve **83% Precision**, thereby drastically minimizing false-positive user bans.
+
+## How to Get Started with the Model
+ToxSense requires a strictly formatted JSON input containing `ocr_text`, `image_caption` (extracted via BLIP), and base `toxicity_scores`. 
+
+```python
+import json
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "yugbirla/ToxSense-json-ultimate"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+
+# 1. Prepare the JSON Payload
+input_data = {
+    "ocr_text": "Look at this completely normal picture.",
+    "image_caption": "A controversial political figure.",
+    "toxicity_scores": {"safe": 0.9, "hate": 0.1}
+}
+
+sys_msg = (
+    "You are ToxSense, a highly intelligent safety moderator. "
+    "You will receive input as a JSON object containing 'ocr_text', 'image_caption', and 'toxicity_scores'. "
+    "Think step-by-step. Analyze the contrast between the text and the image. "
+    "Classify the input into exactly ONE of these categories: "
+    "[safe, racism, sexism, threat, harassment, insult]. "
+    "Output JSON ONLY in this format: {\"reasoning\": \"your short analysis\", \"category\": \"<category_name>\"}"
+)
+
+messages = [
+    {"role": "system", "content": sys_msg}, 
+    {"role": "user", "content": json.dumps(input_data, indent=2)}
+]
+
+# 2. Generate
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([text], return_tensors="pt").to("cuda")
+out = model.generate(**inputs, max_new_tokens=150)
+
+print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))