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Model: ahelkadyy/Qwen3-8B-UnBias-Plus-SFT-Instruct-4bit Source: Original Platform
2026-10-04 20:50:11 +08:00
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+---
+license: apache-2.0
+base_model: vector-institute/Qwen3-8B-UnBias-Plus-SFT-Instruct
+language:
+- en
+tags:
+- qwen3
+- bias-detection
+- news-debiasing
+- fine-tuned
+- 4-bit
+- quantized
+- sft
+- conversational
+pipeline_tag: text-generation
+---
+
+# Qwen3-8B-UnBias-Plus-SFT-Instruct (4-bit)
+
+A **4-bit pre-quantized** version of [ahelkadyy/Qwen3-8B-UnBias-Plus-SFT-Instruct](https://huggingface.co/ahelkadyy/Qwen3-8B-UnBias-Plus-SFT-Instruct), itself a retrained derivative of [vector-institute/Qwen3-8B-UnBias-Plus-SFT-Instruct](https://huggingface.co/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
+
+```python
+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
+
+```python
+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
+
+```json
+{
+  "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](https://huggingface.co/datasets/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](https://github.com/VectorInstitute/unbias-plus)
+- 📊 Dataset: [vector-institute/Unbias-plus](https://huggingface.co/datasets/vector-institute/Unbias-plus)
+- 🏛️ Organization: [Vector Institute](https://vectorinstitute.ai)
+- 🤖 16-bit version: [ahelkadyy/Qwen3-8B-UnBias-Plus-SFT-Instruct](https://huggingface.co/ahelkadyy/Qwen3-8B-UnBias-Plus-SFT-Instruct)
+- 🤖 Original model: [vector-institute/Qwen3-8B-UnBias-Plus-SFT-Instruct](https://huggingface.co/vector-institute/Qwen3-8B-UnBias-Plus-SFT-Instruct)