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--- |
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base_model: FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- qwen2 |
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- trl |
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- reason |
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- Chain-of-Thought |
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- deep thinking |
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license: apache-2.0 |
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language: |
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- en |
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datasets: |
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- bespokelabs/Bespoke-Stratos-17k |
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- Daemontatox/Deepthinking-COT |
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- Daemontatox/Qwqloncotam |
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- Daemontatox/Reasoning_am |
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library_name: transformers |
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--- |
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# **FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview (Fine-Tuned)** |
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## **Model Overview** |
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This model is a fine-tuned version of **FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview**, based on the **Qwen2** architecture. It has been optimized using **Unsloth** for significantly improved training efficiency, reducing compute time by **2x** while maintaining high performance across various NLP benchmarks. |
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Fine-tuning was performed using **Hugging Face’s TRL (Transformers Reinforcement Learning) library**, ensuring adaptability for **complex reasoning, natural language generation (NLG), and conversational AI** tasks. |
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## **Model Details** |
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- **Developed by:** Daemontatox |
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- **Base Model:** [FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview) |
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- **License:** Apache-2.0 |
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- **Model Type:** Qwen2-based large-scale transformer |
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- **Optimization Framework:** [Unsloth](https://github.com/unslothai/unsloth) |
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- **Fine-tuning Methodology:** LoRA (Low-Rank Adaptation) & Full Fine-Tuning |
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- **Quantization Support:** 4-bit and 8-bit for deployment on resource-constrained devices |
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- **Training Library:** [Hugging Face TRL](https://huggingface.co/docs/trl/) |
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## **Training & Fine-Tuning Details** |
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### **Optimization with Unsloth** |
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Unsloth significantly accelerates fine-tuning by reducing memory overhead and improving hardware utilization. The model was fine-tuned **twice as fast** as conventional methods, leveraging **Flash Attention 2** and **PagedAttention** for enhanced performance. |
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### **Fine-Tuning Method** |
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The model was fine-tuned using **parameter-efficient techniques**, including: |
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- **QLoRA (Quantized LoRA)** for reduced memory usage. |
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- **Full fine-tuning** on select layers to maintain original capabilities while improving specific tasks. |
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- **RLHF (Reinforcement Learning with Human Feedback)** for improved alignment with human preferences. |
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## **Intended Use & Applications** |
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### **Primary Use Cases** |
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- **Conversational AI**: Enhances chatbot interactions with **better contextual awareness** and logical coherence. |
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- **Text Generation & Completion**: Ideal for **content creation**, **report writing**, and **creative writing**. |
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- **Mathematical & Logical Reasoning**: Can assist in **education**, **problem-solving**, and **automated theorem proving**. |
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- **Research & Development**: Useful for **scientific research**, **data analysis**, and **language modeling experiments**. |
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### **Deployment** |
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The model supports **4-bit and 8-bit quantization**, making it **deployable on resource-constrained devices** while maintaining high performance. |
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## **Limitations & Ethical Considerations** |
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### **Limitations** |
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- **Bias & Hallucination**: The model may still **generate biased or hallucinated outputs**, especially in **highly subjective** or **low-resource** domains. |
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- **Computation Requirements**: While optimized, the model **still requires significant GPU resources** for inference at full precision. |
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- **Context Length Constraints**: Long-context understanding is improved, but **performance may degrade** on extremely long prompts. |
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### **Ethical Considerations** |
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- **Use responsibly**: The model should not be used for **misinformation**, **deepfake generation**, or **harmful AI applications**. |
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- **Bias Mitigation**: Efforts have been made to **reduce bias**, but users should **validate outputs** in sensitive applications. |
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## **How to Use the Model** |
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### **Example Code for Inference** |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "Daemontatox/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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input_text = "Explain the significance of reinforcement learning in AI." |
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inputs = tokenizer(input_text, return_tensors="pt") |
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output = model.generate(**inputs, max_length=200) |
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print(tokenizer.decode(output[0], skip_special_tokens=True)) |
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Using with Unsloth (Optimized LoRA Inference) |
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from unsloth import FastAutoModelForCausalLM |
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model = FastAutoModelForCausalLM.from_pretrained( |
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"Daemontatox/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview", |
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load_in_4bit=True # Efficient deployment |
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) |
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Acknowledgments |
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Special thanks to: |
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Unsloth AI for their efficient fine-tuning framework. |
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The open-source AI community for continuous innovation. |
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