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Model: yugbirla/toxsense-json-ultimate Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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license: mit
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language:
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- en
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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- FacebookAI/roberta-base
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---
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# Model Card for ToxSense (Multimodal Adversarial Safety Moderator)
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## Model Details
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### Model Description
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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.
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* **Developed by:** Yug Birla
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* **Model type:** Causal Language Model (Fine-tuned for strict JSON-In/JSON-Out Multimodal Reasoning)
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* **Language(s) (NLP):** English
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* **License:** MIT
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* **Finetuned from model:** Open-source Qwen Base Architecture
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### Model Sources
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* **Demo:** [ToxSense Safety Center Dashboard](https://huggingface.co/spaces/yugbirla/ToxSense-Safety-Center)
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### Model Architecture and Objective
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* **Text Engine:** Qwen base + LoRA adapters (Merged)
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* **Vision Dependency:** Designed to ingest outputs from Salesforce BLIP and EasyOCR.
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* **Optimization:** DPO + SFT
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*
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## Uses
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### Direct Use
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* API-ready content moderation for detecting nuanced hate speech in memes and multimodal posts.
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* Multi-category classification (e.g., Harassment, Racism, Threat, Insult, Sexism).
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* Generating transparent "Chain-of-Thought" reasoning for why a post was flagged.
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### Downstream Use
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* Integration into Trust & Safety dashboards for social media platforms.
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* Assisting human moderators by pre-filtering and providing contextual explanations for flagged content.
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### Out-of-Scope Use
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* Fully automated, zero-human-in-the-loop bans for highly ambiguous cases.
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* Medical, legal, or strictly unimodal text classification without proper prompt formatting.
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## Bias, Risks, and Limitations
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**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.
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## How to Get Started with the Model
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ToxSense requires a strictly formatted JSON input containing `ocr_text`, `image_caption` (extracted via BLIP), and base `toxicity_scores`.
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```python
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import json
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "yugbirla/ToxSense-json-ultimate"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
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# 1. Prepare the JSON Payload
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input_data = {
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"ocr_text": "Look at this completely normal picture.",
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"image_caption": "A controversial political figure.",
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"toxicity_scores": {"safe": 0.9, "hate": 0.1}
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}
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sys_msg = (
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"You are ToxSense, a highly intelligent safety moderator. "
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"You will receive input as a JSON object containing 'ocr_text', 'image_caption', and 'toxicity_scores'. "
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"Think step-by-step. Analyze the contrast between the text and the image. "
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"Classify the input into exactly ONE of these categories: "
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"[safe, racism, sexism, threat, harassment, insult]. "
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"Output JSON ONLY in this format: {\"reasoning\": \"your short analysis\", \"category\": \"<category_name>\"}"
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)
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messages = [
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{"role": "system", "content": sys_msg},
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{"role": "user", "content": json.dumps(input_data, indent=2)}
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]
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# 2. Generate
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer([text], return_tensors="pt").to("cuda")
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out = model.generate(**inputs, max_new_tokens=150)
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print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
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54
chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# 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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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61
config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "float16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 3584,
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"initializer_range": 0.02,
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"intermediate_size": 18944,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 28,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
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"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": false,
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"transformers_version": "5.12.1",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 152064
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}
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generation_config.json
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"repetition_penalty": 1.05,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "5.12.1"
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 15231271816
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tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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size 11421892
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tokenizer_config.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [
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"<|im_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>"
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],
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"is_local": true,
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"local_files_only": false,
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"model_max_length": 131072,
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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