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