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Model: Mikkkaiikkk/DeepSeek-R1-Distill-Alpaca-FineTuned
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---
base_model: unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
datasets:
- yahma/alpaca-cleaned
---
# DeepSeek-R1 Alpaca Fine-Tuned Model
## Model Overview
The `DeepSeek-R1 Alpaca Fine-Tuned Model` is a powerful large language model optimized for generating accurate, context-aware responses to domain-specific queries. Fine-tuned on Alpaca using specialized techniques, this model is tailored for advanced natural language understanding and generation tasks.
This fine-tuned model builds upon the foundational strengths of Alpaca while improving adaptability for research and enterprise applications, delivering enhanced performance and accuracy for custom use cases.
## Key Features
- 🚀 **Enhanced Response Quality:** Optimized for detailed and coherent language generation.
- 📚 **Domain Adaptability:** Effective for specific domain knowledge applications.
- 🔧 **Robust Fine-Tuning:** Built using efficient fine-tuning practices as described in [DeepSeek Fine-Tuning Made Simple](https://medium.com/@krishanw30/deepseek-fine-tuning-made-simple-create-custom-ai-models-with-python-7b98f091c824).
- ⚡ **ONNX Runtime for Inference:** Deployed using ONNX Runtime for lightning-fast and efficient inference.
## Training Details
- 🧠 **Base Model:** Alpaca
- 🛠️ **Training Framework:** DeepSeek framework leveraging best-in-class ML practices.
- ⚙️ **Inference:** ONNX Runtime
- 📊 **Data Augmentation:** Customized datasets aligned with the target domain.
- 🖥️ **Hardware Utilized:** High-performance GPUs for accelerated training.
### Fine-Tuning Approach
The model was fine-tuned using the DeepSeek approach, ensuring:
- ✅ Minimal hallucination rates
- 🎯 Task-specific specialization
- 🌍 Maximized generalization capability for unseen tasks
For a detailed walkthrough, please refer to [this article on Medium](https://medium.com/@krishanw30/deepseek-fine-tuning-made-simple-create-custom-ai-models-with-python-7b98f091c824).
## Intended Use Cases
- 🤖 **Custom AI Assistants:** Ideal for tailored customer support models.
- ✍️ **Content Generation:** Crafting specialized content for blogs and documentation.
- 📈 **Data Analysis:** Automating insights extraction.
## Performance Metrics
The fine-tuned model achieves state-of-the-art performance metrics on specialized datasets:
- 📋 **Accuracy:** Improved task-specific precision
- ⚡ **Efficiency:** Reduced latency during inference with ONNX Runtime
## Usage
To use this model, install the required packages and load the model directly from the Hugging Face Hub:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import onnxruntime
# Load Model and Tokenizer
model_name = "krishanwalia30/deepseek-r1-alpaca-finetuned"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example Query
input_text = "What is the best way to fine-tune an AI model?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
response = tokenizer.decode(outputs[0])
print(response)
```
## Limitations
- 🚫 Not suitable for tasks outside its fine-tuned domain
- ⚠️ Requires responsible use in generating accurate and ethical content
## Acknowledgments
Thanks to the ongoing contributions from the ML community and readers who engage with the insights shared on Medium.
## Citation
If you use this model, please cite the work as follows:
```bibtex
@article{DeepSeekFineTuning,
author = {Krishan Walia},
title = {DeepSeek Fine-Tuning Made Simple},
year = {2025},
journal = {Medium},
url = {https://medium.com/@krishanw30/deepseek-fine-tuning-made-simple-create-custom-ai-models-with-python-7b98f091c824}
}
```
We hope this model accelerates your AI development projects!
# Uploaded model
- **Developed by:** krishanwalia30
- **License:** apache-2.0
- **Finetuned from model :** unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

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special_tokens_map.json Normal file
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{
"bos_token": {
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"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"eos_token": {
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"normalized": false,
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"single_word": false
},
"pad_token": {
"content": "<|finetune_right_pad_id|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

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tokenizer.json Normal file
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2068
tokenizer_config.json Normal file
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