Improve model card: Add paper abstract and code link; update pipeline tag

This PR updates the model card for `Tiny-Agent-a-3B` by:

- Adding the abstract of the DynaSaur paper.
- Updating the `pipeline_tag` from `robotics` to `text-generation` to ensure the model is discoverable under general text generation tasks while retaining `robotics` as a specific tag.
- Adding a link to the official DynaSaur GitHub repository for broader context.

README.md

@@ -7,7 +7,7 @@ library_name: transformers
 license: other
 license_name: qwen-research
 license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct/blob/main/LICENSE
-pipeline_tag: robotics
+pipeline_tag: text-generation
 tags:
 - code
 - chat
@@ -19,6 +19,12 @@ tags:
 
 ***Tiny-Agent-α*** is an extension of Dria-Agent-a, trained on top of the [Qwen2.5-Coder](https://huggingface.co/collections/Qwen/qwen25-coder-66eaa22e6f99801bf65b0c2f) series to be used in edge devices. These models are carefully fine tuned with quantization aware training to minimize performance degradation after quantization. 
 
+**DynaSaur GitHub repository:** https://github.com/adobe-research/dynasaur
+
+**Paper abstract:**
+
+Existing LLM agent systems typically select actions from a fixed and predefined set at every step. While this approach is effective in closed, narrowly scoped environments, it presents two major challenges for real-world, open-ended scenarios: (1) it significantly restricts the planning and acting capabilities of LLM agents, and (2) it requires substantial human effort to enumerate and implement all possible actions, which is impractical in complex environments with a vast number of potential actions. To address these limitations, we propose an LLM agent framework that can dynamically create and compose actions as needed. In this framework, the agent interacts with its environment by generating and executing programs written in a general-purpose programming language. Moreover, generated actions are accumulated over time for future reuse. Our extensive experiments across multiple benchmarks show that this framework significantly improves flexibility and outperforms prior methods that rely on a fixed action set. Notably, it enables LLM agents to adapt and recover in scenarios where predefined actions are insufficient or fail due to unforeseen edge cases. Our code can be found in this https URL .
+
 Tiny-Agent-α employs ***Pythonic function calling***, which is LLMs using blocks of Python code to interact with provided tools and output actions. This method was inspired by many previous work, including but not limited to [DynaSaur](https://arxiv.org/pdf/2411.01747), [RLEF](https://arxiv.org/pdf/2410.02089), [ADAS](https://arxiv.org/pdf/2408.08435) and [CAMEL](https://arxiv.org/pdf/2303.17760). This way of function calling has a few advantages over traditional JSON-based function calling methods:
 
 1. **One-shot Parallel Multiple Function Calls:** The model can can utilise many synchronous processes in one chat turn to arrive to a solution, which would require other function calling models multiple turns of conversation.