Update README.md

README.md

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     <a href="https://huggingface.co/imsanjoykb/deepSQL-R1-distill-8B" target="_blank" style="margin: 2px;">
         <img alt="Hugging Face Model" src="https://img.shields.io/badge/HuggingFace-Model-FF6F00?style=for-the-badge&logo=huggingface&logoColor=white" style="display: inline-block; vertical-align: middle;">
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 ## Abstract
 State-of-the-art advances in LLMs have pushed NLP to its limits, where even complex tasks, such as code generation, can be automated. This paper describes the deepSQL-R1-distill-8B, a fine-tuned and quantized model variant of the DeepSeek-R1 model architecture and specifically optimized for text-to-SQL conversion. Fine-tuning was performed using Unsloth, one of the most efficient frameworks for fine-tuning LLMs, in combination with Parameter-Efficient Fine-Tuning and the SFTTrainer framework. This allows domain-specific adaptation with minimal resource consumption. The approach fine-tunes curated datasets by LoRA, ensuring a more parameter-efficient and lower-memory-consuming model. Besides this, we investigate reinforcement learning techniques to further enhance the model's ability in generating accurate and contextually appropriate SQL queries. Combination of 8-bit quantization, LoRA, Unsloth, and reinforcement learning places deepSQL-R1-distill-8B as one of the cutting-edge solutions for automatic SQL code generation in real-world applications.  Addressing major challenges in computational efficiency, domain-specific adaptation, and reinforcement-based refinement, this model is leading the way toward a more intuitive and resource-effective way of interacting with relational databases.
 
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 | 5️⃣  | llama3.2                    | 75                         | 75                | 77                       | 72                          | 82                  | 76                  | 74                  | 71                  | 77                   | 74                        |
 | 6️⃣  | Mistral-7B                  | 70                         | 70                | 72                       | 68                          | 78                  | 72                  | 70                  | 68                  | 72                   | 70                        |
 
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 ## Inference
 
 Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
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 _ = model.generate(**inputs, streamer=text_streamer, max_new_tokens=350)
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 ## Citing
 ```
 @misc{,
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 ## Usages Services
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