Instructions to use arunsingh80475/qwen2.5-14b-humanizer-v4-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arunsingh80475/qwen2.5-14b-humanizer-v4-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-14B-Instruct")
model = PeftModel.from_pretrained(base_model, "arunsingh80475/qwen2.5-14b-humanizer-v4-lora")

Transformers

How to use arunsingh80475/qwen2.5-14b-humanizer-v4-lora with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="arunsingh80475/qwen2.5-14b-humanizer-v4-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("arunsingh80475/qwen2.5-14b-humanizer-v4-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use arunsingh80475/qwen2.5-14b-humanizer-v4-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arunsingh80475/qwen2.5-14b-humanizer-v4-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arunsingh80475/qwen2.5-14b-humanizer-v4-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/arunsingh80475/qwen2.5-14b-humanizer-v4-lora

SGLang

How to use arunsingh80475/qwen2.5-14b-humanizer-v4-lora with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "arunsingh80475/qwen2.5-14b-humanizer-v4-lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arunsingh80475/qwen2.5-14b-humanizer-v4-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "arunsingh80475/qwen2.5-14b-humanizer-v4-lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arunsingh80475/qwen2.5-14b-humanizer-v4-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use arunsingh80475/qwen2.5-14b-humanizer-v4-lora with Docker Model Runner:
```
docker model run hf.co/arunsingh80475/qwen2.5-14b-humanizer-v4-lora
```

🛡️ Qwen2.5-14B-Humanizer-v4-LoRA: Airtight Fact-Preserving Anti-AI Model

qwen2.5-14b-humanizer-v4-lora is a high-precision, fine-tuned Low-Rank Adaptation (LoRA) adapter built upon Qwen/Qwen2.5-14B-Instruct. It transforms robotic, low-perplexity, stereotypical AI-generated text into fluid, nuanced, and engaging human-grade prose while enforcing an airtight 100% mathematical and factual lock.

Developed by Arun Singh, this Version 4 model is the result of a complete overhaul of previous humanizer iterations, addressing the most critical flaw in modern paraphrasers and AI humanizers: numerical drift, currency alteration, and subtle factual hallucination.

⚡ What Makes V4 Different? (The Airtight Fact-Lock Architecture)

Earlier humanization models and commercial paraphrasers often corrupt quantitative details (for example, truncating $4.2 million into $4M or rounding 18.4% to ~18%). Version 4 introduces an engineered dataset curation and alignment protocol that completely eliminates these leaks.

Challenge	Version 3 Behavior	Version 4 Solution
Substring False Matches	Substring checks allowed `'7'` to match inside `'1987'`, passing bad training pairs.	Word-Boundary Match: Strict regex (`\b\d+\b`) prevents substring false positives.
Currency & Unit Rounding	Truncated complex metrics (`$4.2 million` $\rightarrow$ `$4M`).	Atomic Metric Locking: Multi-token currencies, decimals, and magnitudes are locked as indivisible units.
Corrupted Tokens	Leaked broken UTF-8 sequences (`\ufffd`, mojibake currency symbols).	Unicode Sanitization Pass: Complete multi-byte cleansing and character normalization.
Robotic Transition Habits	SFT alone left subtle transitional filler intact ("Moreover", "Furthermore").	Dual SFT + DPO Alignment: Preference optimization directly penalizes formulaic AI phrases.
Numerical Hallucination	System prompt vaguely requested factual accuracy.	Strict Zero-Tolerance Prompt: Explicitly forbids rounding, estimation, and truncation.
Adaptive Decoding	Fixed high temperature caused metric drift.	Adaptive Temperature Regime: `0.48` for financial/technical data; `0.72` for general prose.

🎯 Model Details

Developed by: Arun Singh (@arunsingh80475)
Model Type: Causal Language Model Adapter (PEFT / LoRA)
Base Model: Qwen/Qwen2.5-14B-Instruct
Languages: English (en)
License: Apache 2.0
Fine-Tuning Regimes: Two-Stage Pipeline (Supervised Fine-Tuning + Direct Preference Optimization)
Primary Objective: Invert artificial syntax, restore natural burstiness/perplexity, and eliminate AI clichéd markers without semantic or numerical distortion.

🔬 Anti-AI Quality Gates & Elimination Policy

The training data and preference triplets were filtered against a strict composite anti-AI rulebook combining two leading industry frameworks:

1. Wikipedia 35 Signs of AI Generation

Participial Overload: Eliminates synthetic dangling comma-ing clauses (", thereby showcasing...", ", facilitating...").
Em-Dash Overuse: Enforces zero em-dashes (—) or synthetic parenthetical interruptions.
Formulaic Openings & Closings: Bans stiff transitions ("Furthermore", "Moreover", "In conclusion", "In summary").

2. Conor Bronsdon "Avoid-AI-Writing" Standard

Tier 1A AI Buzzwords (Permanently Suppressed):
- delve, tapestry, realm, paradigm, beacon, bespoke, testament, leverage, cornerstone
- pivotal, watershed moment, game-changer, seamless, meticulous, deep dive, unpack
- ever-evolving, holistic, actionable, interplay, burgeoning, nascent, empower, foster
Tier 1B Wordiness & Stiff Copulas:
- Bans "due to the fact that", "serves as", "boasts", "commence", "in order to".
Burstiness Variance Requirement:
- Sentence length variance enforced at $\ge 15.0$ to restore authentic human cadence, natural rhythm, and high perplexity.

📊 Dataset & Self-Audit Statistics

The model was trained on an exclusively audited gold dataset comprising 3,000 gold parallel pairs and 2,850 preference triplets:

===========================================================================
  14B v4 DATASET CURATION & AUDIT REPORT
===========================================================================
  Total Curated Gold Pairs         : 3,000
    • Fact & Number-Locked Pairs    : 2,076
    • General Editorial Rewrites    : 924
    • Train Split (95%)             : 2,850 pairs
    • Validation Split (5%)         : 150 pairs
    • DPO Preference Triplets       : 2,850 triplets

  Rejected by V4 Quality Gates:
    • Currency Rounding / Drift     : 224 pairs rejected (were accepted in v3)
    • Substring False Retention     : 51 pairs rejected
    • AI Signs Quality Gate         : 1,897 pairs rejected
    • Corrupted Unicode Artifacts   : Sanitized / Purged

  Self-Audit Verification:
    • Numerical & Metric Mismatches : 0 across all 2,850 pairs (100% Pass) ✅
===========================================================================

🛠️ Hyperparameters & Training Specifications

The model was fine-tuned in a two-stage sequential pipeline on Dual NVIDIA Tesla T4 GPUs (16GB x 2) via Kaggle in ~20 minutes total runtime:

LoRA Configuration

Rank ($r$): 16
Alpha ($\alpha$): 32
Dropout: 0.05
Bias: none
Target Modules: All linear attention and MLP projections (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj)
Precision: Active adapter parameters in float32; base model in 4-bit NF4 (load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True)

Phase 1: Supervised Fine-Tuning (SFT)

Optimizer: paged_adamw_8bit
Learning Rate: 1.5e-4 (Cosine decay schedule)
Warmup Steps: 25
Batch Size: Per-device 1 with 4 gradient accumulation steps (Effective batch size = 4)
Sequence Length: 640 tokens with ChatML template
Training Time: ~12 minutes

Phase 2: Direct Preference Optimization (DPO)

Trainer: TRL DPOTrainer with implicit reference model (zero additional VRAM footprint)
DPO $\beta$: 0.1
Learning Rate: 5.0e-5 (Cosine schedule)
Warmup Steps: 20
Batch Size: Per-device 1 with 8 gradient accumulation steps (Effective batch size = 8)
Training Time: ~8 minutes

💻 Quick Start & Inference

1. Installation

pip install -U torch transformers peft bitsandbytes accelerate

2. Python Inference Code (4-bit NF4 Quantized)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

BASE_MODEL_ID = "Qwen/Qwen2.5-14B-Instruct"
ADAPTER_ID = "arunsingh80475/qwen2.5-14b-humanizer-v4-lora"

# 1. Load Tokenizer
tokenizer = AutoTokenizer.from_pretrained(ADAPTER_ID, trust_remote_code=True)

# 2. Configure 4-bit quantization for efficient GPU memory usage
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
)

# 3. Load Base Model and LoRA Adapter
base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL_ID,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(base_model, ADAPTER_ID)
model.eval()

# 4. Strict Fact-Preservation System Prompt
SYSTEM_PROMPT = (
    "You are an expert human writer and editor. Rewrite the input text into natural, clear, and engaging human prose.\n"
    "STRICT COMPLIANCE RULES:\n"
    "1. ABSOLUTE NUMBER & METRIC LOCK: Preserve 100% of all numbers, decimals, percentages, dates, and currencies VERBATIM. "
    "NEVER round, truncate, or approximate figures (e.g. keep '$4.2 million' exactly as '$4.2 million', NEVER convert to '$4M'; keep '18.4%' exactly, NEVER '~18%').\n"
    "2. FLAWLESS GRAMMAR: Ensure strict subject-verb agreement and natural sentence flow.\n"
    "3. NATURAL CADENCE: Eliminate robotic transitions ('Furthermore', 'Moreover', 'In conclusion') and commercial clichés "
    "('testament', 'cornerstone', 'delve', 'pivotal') without becoming overly colloquial.\n"
    "4. Output ONLY the direct rewritten text. No preambles, no explanations."
)

def humanize(text: str, temperature: float = 0.48) -> str:
    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": text}
    ]
    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=512,
            temperature=temperature,
            top_p=0.92,
            repetition_penalty=1.12,
            do_sample=True,
        )

    response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
    return response.strip()

# Example: Challenging corporate / financial input
sample_input = (
    "Our company's financial performance in Q3 stands as a testament to strategic resilience. "
    "Overall revenue increased by 18.4% to $4.2 million, largely driven by the launch of our cloud platform. "
    "Furthermore, by optimizing onboarding workflows, client churn decreased from 6.2% to 3.8% over the past six months. "
    "In conclusion, operational discipline serves as a foundational cornerstone for sustained profitability."
)

print(humanize(sample_input))

📈 Recommended Generation Settings

Domain / Input Type	Temperature	Top-P	Repetition Penalty	Rationale
Financial / Quantitative	`0.45 – 0.48`	`0.90`	`1.12`	Prevents token divergence and locks exact currencies and numbers.
Technical / Scientific	`0.48 – 0.52`	`0.92`	`1.12`	Preserves exact formulas, acronyms, and terminology.
Articles / Essays	`0.68 – 0.72`	`0.94`	`1.10`	Higher burstiness and rhythmic sentence length variety.
Creative / Narrative	`0.72 – 0.78`	`0.95`	`1.08`	Idiomatic variety and expressive prose.

🧪 Live Verification Benchmark

Input (Standard LLM Generation):

"Our company's financial performance in Q3 stands as a testament to strategic resilience. Overall revenue increased by 18.4% to $4.2 million, largely driven by the launch of our cloud platform. Furthermore, by optimizing onboarding workflows, client churn decreased from 6.2% to 3.8% over the past six months. In conclusion, operational discipline serves as a foundational cornerstone for sustained profitability."

V4 Humanized Output:

"Q3 financial results reflect strong operational resilience. Driven by the new cloud platform, revenue grew 18.4% to $4.2 million. Refined onboarding workflows also helped lower customer churn from 6.2% to 3.8% across the last six months. Disciplined execution continues to anchor long-term profitability."

Automated Verification Audit:

✅ $4.2 million preserved verbatim (not rounded to $4M)
✅ 18.4% preserved verbatim (not approximated to ~18%)
✅ 6.2% and 3.8% preserved verbatim
✅ Zero banned clichés: "testament", "cornerstone", "furthermore", and "in conclusion" completely eradicated
✅ Zero em-dashes (—)

🛡️ Intended Uses & Limitations

Intended Uses:

Academic and Technical Revision: Polishing technical papers, research briefs, and theses to read organically while preserving formulas, numbers, and data points.
Enterprise & Business Communications: Transforming draft reports, executive memos, and newsletters into crisp, engaging human prose.
Content Creation: Enhancing blogs, case studies, and articles with dynamic rhythm and varied sentence lengths.
Data Privacy: 100% on-device local execution—zero sensitive client or proprietary data sent to third-party cloud APIs.

Limitations & Ethical Considerations:

Language Scope: Optimized specifically for English text.
Ethical Attribution: Not intended to misrepresent authored work where explicit attribution of AI assistance is required by institutional, journalistic, or academic policy.
Extreme Domain Formulations: For highly specialized code snippets or mathematical proofs embedded in raw text, use lower temperatures (0.3 – 0.45) to prevent syntax alterations.

📑 Citation & Contact

If you use this model or pipeline in your research or applications, please cite:

@misc{singh2024qwen14bhumanizer,
  author = {Arun Singh},
  title = {Qwen2.5-14B-Humanizer-v4-LoRA: Airtight Fact-Preserving Anti-AI Model},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/arunsingh80475/qwen2.5-14b-humanizer-v4-lora}}
}

Developer Contact: Arun Singh on Hugging Face

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