Qwen3_8B_NoThink-NF4

🔍 Model Summary

Qwen3_8B_NoThink-NF4 is a modified and optimized variant of
huihui-ai/Huihui-Qwen3-8B-abliterated-v2, retrained to eliminate chain-of-thought reasoning, remove <think>-style internal monologue, and produce clean, direct answers.

This version is:

• Fine-tuned on a curated NoThink FLAN-ULTRA dataset
• Designed for deterministic, concise outputs
• LoRA-finetuned, merged, and exported as a dense model
• Fully NF4-quantized for efficient GPU inference
• Published as: ikarius/Qwen3_8B_NoThink-NF4

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🚀 What’s Special About This Model?

🧼 No chain-of-thought — ever

All verbose reasoning traces are removed during training.
The model only gives final answers, no explanations unless explicitly requested.

💬 More natural, more engaging

Training data produces a subtly more expressive and friendly tone compared to the original Qwen3.

⚡ NF4 Quantized

• Fits easily on consumer GPUs (e.g., RTX 4070 / 5070 / 5080 / 4090 / 5090 / 3090)
• Lower VRAM footprint
• Excellent inference speed

🧩 Built for ChatML and general instruction prompts

Although trained with ChatML, it works perfectly with plain-text prompts.

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📦 Model Details

Base Model

Originally:
huihui-ai/Huihui-Qwen3-8B-abliterated-v2

Then:

1. LoRA fine-tuned with NoThink FLAN ULTRA
2. Merged into a dense bf16 model
3. Re-quantized to NF4

Quantization

• load_in_4bit=True
• NF4 quant type
• Double quantization enabled
• compute_dtype = bfloat16

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🧠 Intended Uses

✔ Recommended

• Chat assistants (no chain-of-thought)
• Direct Q&A
• Code generation
• Summaries
• Local inference
• Edge devices or consumer GPUs
• Context-constrained deployments

❌ Not suitable for

• Multi-step reasoning tasks
• Proof-based mathematics
• Internal-deliberation-required tasks
• Safety-critical systems

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🏋️ Training Information

Dataset

nothink_flan_ULTRA.jsonl
~45k examples
Derived from FLAN + custom filtering:

• Removed all <think> and reasoning traces
• Preserved normal adult language
• Removed unsafe sexual content (incest, illegal porn descriptions)
• High diversity: QA, classification, summarization, translations, logic tasks, etc.

Training Method

• QLoRA fine-tuning
• LoRA rank: 64
• LoRA alpha: 128
• Dropout: 0.05
• Learning rate: 1.2e-4
• 1 epoch
• Max seq length: 768 tokens
• Optimizer: paged_adamw_8bit
• Scheduler: cosine
• BF16 training

Hardware

• 1× NVIDIA RTX 5090 32GB
• Peak VRAM usage: ~26–28 GB during training

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📉 Limitations

• May hallucinate factual details
• Reduced deep reasoning ability by design
• Not intended for tasks requiring explained logic
• Tone is slightly friendlier than the base model

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⚖️ License

This model follows the license of Qwen3 and
huihui-ai/Huihui-Qwen3-8B-abliterated-v2.

Users are responsible for ensuring legal compliance.

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▶ Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "ikarius/Qwen3_8B_NoThink-NF4"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype=torch.bfloat16
)

tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Explain what a black hole is in simple terms."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=200)

print(tokenizer.decode(output[0], skip_special_tokens=True))

⭐ Acknowledgements

Qwen3 team

Huihui for the original abliterated variant

Open-source contributors to TRL, PEFT, bitsandbytes

Community research that made chain-of-thought mitigation possible