Daemontatox / QwQ_Zero1 (Unsloth Finetune)

A focused attempt to solve the QwQ-style “reasoning freeze” by finetuning QwQ for fluid, multi-step reasoning — without compromising on depth or capability.

This model is made for creators who want both intelligence and speed in a single interface. Powered by Unsloth and TRL, trained with intention, and built for rapid logical flow.

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Model Details

• Base model: unsloth/qwq-32b-unsloth
• Developer: Daemontatox
• License: apache-2.0
• Architecture: Qwen2.5
• Precision: float16
• Libraries: Unsloth, TRL, Transformers
• Finetuning Goal: Resolve stalling during reasoning while maintaining chain-of-thought fidelity

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Training Details

• Epochs: [5]
• Batch Size: [16]
• Sequence Length: [2048]
• Optimizations: LoRA , gradient checkpointing, Flash Attention 2
• Hardware Used: 3x L40S
• Datasets: Custom curated prompts focused on logical progression, question answering, and instruction flow (details withheld)

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Usage

This model is best suited for:

• Fast, chain-of-thought reasoning in multi-turn dialogue or step-by-step prompts
• Agentic workflows where latency and logical progression are key
• Creative ideation and brainstorming with minimal hallucination under clear prompts
• Instruction following, task decomposition, and in-context problem solving

Example usage:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Daemontatox/QWQ_Zero1" 

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

prompt = "Explain why the moon affects the tides in a step-by-step way."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Intended Use Cases

• Autonomous AI agents with complex task breakdowns
• Education platforms requiring detailed, structured explanations
• LLM pipelines using RAG or retrieval to walk through logical steps
• Dev tools that scaffold reasoning for planning, coding, or content generation

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Limitations

• Not instruction-tuned – prompt structure significantly affects output quality
• High memory requirements – 32B model requires ≥48GB GPU or offloading setups
• Reasoning optimization ≠ factual accuracy – verify claims from output
• No safety alignment – model may reflect biases from pretraining data
• Occasional verbosity – especially in open-ended generative contexts

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Evaluation

Metric	Value
MMLU (Zero-shot)	TBD
ARC Challenge	TBD
Long-form CoT Tasks	TBD
Human Eval	TBD

Evaluation in progress — coming soon.

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Ethical Considerations

This model has not been aligned for safety, fairness, or bias mitigation. It may:

• Reflect harmful stereotypes or cultural bias present in pretraining data
• Produce hallucinated or false information confidently
• Mislead users if used without human oversight

Do not use this model in:

• Medical, legal, financial, or psychological contexts
• Any real-time, high-stakes, or human-critical system

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Future Work

• Instruction tuning for robust multi-task generalization
• Safety alignment via RLHF or constitutional guidance
• Integration into open-agent frameworks and interactive environments
• Adding evaluation dashboards + quantized deployment versions

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Citation

If you use this model, please credit:

Daemontatox –QwQ_Zero1 32B
Built with Unsloth, TRL, and deep love for fast reasoning.

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try it out

Contact

For questions, feedback, or collaboration inquiries, reach out via:

• Hugging Face: @Daemontatox
• GitHub: github.com/Daemontatox

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