star-lab
/

STAR-1b7

@@ -18,7 +18,7 @@ library_name: transformers
 ## Introduction
-**STAR-1b7** is a highly capable 1.7B parameter language model specialized in function calling, achieving excellent performances on the [Berkeley Function Calling Leaderboard (BFCL)](https://huggingface.co/spaces/gorilla-llm/berkeley-function-calling-leaderboard) for models in its size class.
 This model is the result of fine-tuning the `Qwen/Qwen3-1.7B` base model using the novel **STAR (Similarity-guided Teacher-Assisted Refinement)** framework. STAR is a holistic training curriculum designed to effectively transfer the advanced capabilities of large language models (LLMs) into "super-tiny" models, making them powerful, accessible, and efficient for real-world agentic applications.
@@ -26,8 +26,6 @@ The key innovations of the STAR framework include:
 - **Similarity-guided RL (Sim-RL)**: A reinforcement learning mechanism that uses a fine-grained, similarity-based reward signal. This provides a more robust and continuous signal for policy optimization compared to simple binary rewards, which is crucial for complex, multi-solution tasks like function calling.
 - **Constrained Knowledge Distillation (CKD)**: An advanced training objective that augments top-k forward KL divergence to suppress confidently incorrect predictions. This ensures training stability while preserving the model's exploration capacity, creating a strong foundation for the subsequent RL phase.
-Our STAR-1b7 model significantly outperforms other open models under 1B parameters and even surpasses several larger models, demonstrating the effectiveness of the STAR methodology.
 ## Model Details
 - **Model Type**: Causal Language Model, fine-tuned for function calling.
@@ -100,7 +98,7 @@ For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTr
 ## Evaluation & Performance
-STAR-1b7 has achieved outstanding performance for models of its size on renowned function calling benchmarks.
 - BFCLv3: Achieved 56.05% overall accuracy.
 - ACEBench: Achieved 60.90% summary score, demonstrating superior generalization and robustness.

 ## Introduction
+**STAR-1b7** is a highly capable 1.7B parameter language model specialized in function calling.
 This model is the result of fine-tuning the `Qwen/Qwen3-1.7B` base model using the novel **STAR (Similarity-guided Teacher-Assisted Refinement)** framework. STAR is a holistic training curriculum designed to effectively transfer the advanced capabilities of large language models (LLMs) into "super-tiny" models, making them powerful, accessible, and efficient for real-world agentic applications.
 - **Similarity-guided RL (Sim-RL)**: A reinforcement learning mechanism that uses a fine-grained, similarity-based reward signal. This provides a more robust and continuous signal for policy optimization compared to simple binary rewards, which is crucial for complex, multi-solution tasks like function calling.
 - **Constrained Knowledge Distillation (CKD)**: An advanced training objective that augments top-k forward KL divergence to suppress confidently incorrect predictions. This ensures training stability while preserving the model's exploration capacity, creating a strong foundation for the subsequent RL phase.
 ## Model Details
 - **Model Type**: Causal Language Model, fine-tuned for function calling.
 ## Evaluation & Performance
+STAR-1b7 has achieved outstanding performance on renowned function calling benchmarks.
 - BFCLv3: Achieved 56.05% overall accuracy.
 - ACEBench: Achieved 60.90% summary score, demonstrating superior generalization and robustness.