Update README.md

README.md

@@ -1,3 +1,106 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+- zh
+pipeline_tag: text-generation
+base_model: Qwen/Qwen3-4b
+tags:
+- chat
+- function-calling
+- tool-use
+- star-method
+- sota
+library_name: transformers
+---
+
+# STAR-4b
+
+## Introduction
+
+**STAR-4b** is a highly capable 4b 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/Qwen3-4b` 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.
+
+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-4b 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.
+- **Base Model**: `Qwen/Qwen3-4b`
+- **Training Framework**: STAR (CKD + Sim-RL)
+- **Architecture**: Transformer with RoPE, SwiGLU, RMSNorm, and Attention QKV bias.
+- **Number of Parameters**: ~4b
+- **Context Length**: Supports up to 32,768 tokens.
+
+## Requirements
+
+The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
+With `transformers<4.51.0`, you will encounter the following error:
+```
+KeyError: 'qwen3'
+```
+
+## Quickstart
+
+Here is a code snippet showing how to load STAR-4b and use it for a chat-based task.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "star-lab/STAR-4b"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Example prompt that could trigger a function call
+prompt = "What is the current weather in San Francisco?"
+messages = [
+    {"role": "system", "content": "You are a helpful assistant with access to external tools."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=32768
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
+- SGLang:
+    ```shell
+    python -m sglang.launch_server --model-path star-lab/STAR-4b --reasoning-parser qwen3
+    ```
+- vLLM:
+    ```shell
+    vllm serve star-lab/STAR-4b --enable-reasoning --reasoning-parser deepseek_r1
+    ```
+
+For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported STAR-4b.
+
+## Evaluation & Performance
+
+STAR-4b has achieved outstanding performance for models of its size on renowned function calling benchmarks.
+
+- BFCLv3: Achieved 65.24% overall accuracy.
+- ACEBench: Achieved 74.10% summary score, demonstrating superior generalization and robustness.