license, base_model, language, tags, pipeline_tag

license	base_model	language	tags	pipeline_tag
apache-2.0	vector-institute/Qwen3-8B-UnBias-Plus-SFT-Instruct	en	qwen3 bias-detection news-debiasing fine-tuned 4-bit quantized sft conversational	text-generation

Qwen3-8B-UnBias-Plus-SFT-Instruct (4-bit)

A 4-bit pre-quantized version of ahelkadyy/Qwen3-8B-UnBias-Plus-SFT-Instruct, itself a retrained derivative of vector-institute/Qwen3-8B-UnBias-Plus-SFT-Instruct.

This version exists for accessibility — it runs on any GPU with ~6GB VRAM, removing the need for high-end hardware to use the model.

Given a news article, the model identifies biased language segments, classifies their bias type and severity, provides neutral replacements, and returns a fully rewritten unbiased version — all in a single structured JSON response.

Relationship to the Original

	Original (Vector Institute)	16-bit (this lineage)	4-bit (this model)
Model	vector-institute/Qwen3-8B-UnBias-Plus-SFT-Instruct	ahelkadyy/Qwen3-8B-UnBias-Plus-SFT-Instruct	ahelkadyy/Qwen3-8B-UnBias-Plus-SFT-Instruct-4bit
VRAM	~16GB	~16GB	~6GB
Quantization	bf16	bf16	4-bit
Training	Same recipe	Same recipe	Same recipe

The 16-bit and 4-bit versions were retrained using the exact same setup as the Vector Institute original (same dataset, LoRA config, hyperparameters) on the same A100 hardware. The only difference is the 4-bit version is pre-quantized at export time for accessibility.

Model Details

Property	Value
Base model	Qwen/Qwen3-8B
Fine-tuning method	SFT with LoRA
Training precision	bf16 (no quantization during training)
LoRA rank	16, alpha 32, rSLoRA
Training framework	Unsloth + TRL
Context length	8192 tokens
Thinking mode	Disabled (enable_thinking=False)
Output format	Structured JSON
Quantization	4-bit (post-training)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch, json

model_id = "ahelkadyy/Qwen3-8B-UnBias-Plus-SFT-Instruct-4bit"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    load_in_4bit=True,
    device_map="auto",
)
model.eval()

SYSTEM_PROMPT = """You are an expert linguist and bias detection specialist.
Your task is to carefully read a news article, detect ALL biased language,
and return a structured JSON response. Return ONLY valid JSON, no extra text."""

article = "Your news article here..."

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": f"Analyze the following article for bias and return the result in the required JSON format.\n\nARTICLE:\n{article}"},
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    enable_thinking=False,
    return_tensors="pt",
    return_dict=True,
    truncation=True,
    max_length=8192,
)

with torch.no_grad():
    outputs = model.generate(
        input_ids=inputs["input_ids"].to(model.device),
        attention_mask=inputs["attention_mask"].to(model.device),
        max_new_tokens=4096,
        do_sample=False,
        temperature=None,
        top_p=None,
        pad_token_id=tokenizer.eos_token_id,
    )

new_tokens = outputs[0][inputs["input_ids"].shape[1]:]
response = tokenizer.decode(new_tokens, skip_special_tokens=True)
result = json.loads(response)

Using with the UnBias-Plus toolkit

from unbias_plus import UnBiasPlus

pipe = UnBiasPlus(
    model_name_or_path="ahelkadyy/Qwen3-8B-UnBias-Plus-SFT-Instruct-4bit",
    load_in_4bit=True,
)

result = pipe.analyze("Your article text here...")
print(result.binary_label)
print(result.severity)
print(result.unbiased_text)

Output Schema

{
  "binary_label": "biased" | "unbiased",
  "severity": 0 | 2 | 3 | 4,
  "bias_found": true | false,
  "biased_segments": [
    {
      "original": "exact substring from input article",
      "replacement": "neutral alternative phrase",
      "severity": "high" | "medium" | "low",
      "bias_type": "loaded language | dehumanizing framing | false generalizations | framing bias | euphemism/dysphemism | politically charged terminology | sensationalism",
      "reasoning": "1-2 sentence explanation"
    }
  ],
  "unbiased_text": "Full rewritten neutral article"
}

Severity Scale

Value	Meaning
0	Neutral — no bias detected
2	Recurring biased framing
3	Strong persuasive tone
4	Inflammatory rhetoric

Training Data

Fine-tuned on vector-institute/Unbias-plus, a curated dataset of news articles with expert-annotated bias labels, segment-level annotations, and neutral rewrites.

Limitations

• Trained primarily on English-language news articles
• Best performance on articles under 5000 characters
• Outputs should be reviewed by a human before use in production

Links

• 🔗 Project: UnBias-Plus on GitHub
• 📊 Dataset: vector-institute/Unbias-plus
• 🏛️ Organization: Vector Institute
• 🤖 16-bit version: ahelkadyy/Qwen3-8B-UnBias-Plus-SFT-Instruct
• 🤖 Original model: vector-institute/Qwen3-8B-UnBias-Plus-SFT-Instruct