Improve model card: Add pipeline tag, library name, paper & code links (#1)

- Improve model card: Add pipeline tag, library name, paper & code links (b3ad0189c35d46bcf321bf9ca794691c5ac2eea5)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,13 +1,20 @@
 ---
-license: apache-2.0
+base_model:
+- Qwen/Qwen3-1.7B
 datasets:
 - codefuse-ai/F2LLM
 language:
 - en
-base_model:
-- Qwen/Qwen3-1.7B
+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 model is presented in the paper [F2LLM Technical Report: Matching SOTA Embedding Performance with 6 Million Open-Source Data](https://huggingface.co/papers/2510.02294).
+The code for this model is available on [GitHub](https://github.com/codefuse-ai/CodeFuse-Embeddings/tree/main/F2LLM).
+
 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
@@ -84,4 +91,4 @@ If you use the F2LLM models, data, or code, please cite the following technical
   eprinttype    = {arXiv},
   eprint       = {2510.02294}
 }
-```
+```