Improve model card: Add pipeline tag, library name, and paper link

This PR enhances the model card for the F2LLM-0.6B model by:
- Adding the `pipeline_tag: feature-extraction` to the YAML metadata, which improves the model's discoverability for embedding tasks on the Hugging Face Hub.
- Adding the `library_name: transformers` to the YAML metadata. This is justified by the existing `Usage` section's code snippet, which utilizes `transformers.AutoModel` and `AutoTokenizer`, and will enable the automatic "How to use" widget on the model page.
- Setting the main title of the model card to reflect the paper's title and adding a direct link to the paper, providing a clearer overview and easier access to the research.

README.md

@@ -1,13 +1,19 @@
 ---
-license: apache-2.0
+base_model:
+- Qwen/Qwen3-0.6B
 datasets:
 - codefuse-ai/F2LLM
 language:
 - en
-base_model:
-- Qwen/Qwen3-0.6B
+license: apache-2.0
+pipeline_tag: feature-extraction
+library_name: transformers
 ---
 
+# F2LLM Technical Report: Matching SOTA Embedding Performance with 6 Million Open-Source Data
+
+This repository contains the F2LLM-0.6B model presented in the paper [F2LLM Technical Report: Matching SOTA Embedding Performance with 6 Million Open-Source Data](https://huggingface.co/papers/2510.02294).
+
 F2LLMs (Foundation to Feature Large Language Models) are foundation models directly finetuned on 6 million high-quality query-document pairs (available in [codefuse-ai/F2LLM](https://huggingface.co/datasets/codefuse-ai/F2LLM)) covering a diverse range of retrieval, classification, and clustering data, curated solely from open-source datasets without any synthetic data. These models are trained with homogeneous macro batches in a single stage, without sophisticated multi-stage pipelines.
 
 ## Usage