README.md

---
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)