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--- |
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license: apache-2.0 |
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language: |
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- en |
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- zh |
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pipeline_tag: text-generation |
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base_model: Qwen/Qwen3-1.7B |
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tags: |
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- chat |
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- function-calling |
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- tool-use |
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- star-method |
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- sota |
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library_name: transformers |
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--- |
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# STAR-1b7 |
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## Introduction |
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**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. |
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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. |
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The key innovations of the STAR framework include: |
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- **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. |
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- **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. |
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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. |
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## Model Details |
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- **Model Type**: Causal Language Model, fine-tuned for function calling. |
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- **Base Model**: `Qwen/Qwen3-1.7B` |
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- **Training Framework**: STAR (CKD + Sim-RL) |
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- **Architecture**: Transformer with RoPE, SwiGLU, RMSNorm, and Attention QKV bias. |
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- **Number of Parameters**: ~1.7B |
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- **Context Length**: Supports up to 32,768 tokens. |
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## Requirements |
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The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`. |
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With `transformers<4.51.0`, you will encounter the following error: |
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``` |
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KeyError: 'qwen3' |
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``` |
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## Quickstart |
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Here is a code snippet showing how to load STAR-1b7 and use it for a chat-based task. |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "star-lab/STAR-1b7" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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# Example prompt that could trigger a function call |
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prompt = "What is the current weather in San Francisco?" |
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messages = [ |
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{"role": "system", "content": "You are a helpful assistant with access to external tools."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=32768 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(response) |
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``` |
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For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint: |
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- SGLang: |
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```shell |
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python -m sglang.launch_server --model-path star-lab/STAR-1b7 --reasoning-parser qwen3 |
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``` |
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- vLLM: |
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```shell |
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vllm serve star-lab/STAR-1b7 --enable-reasoning --reasoning-parser deepseek_r1 |
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``` |
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For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported STAR-1b7. |
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## Evaluation & Performance |
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STAR-1b7 has achieved outstanding performance for models of its size on renowned function calling benchmarks. |
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- BFCLv3: Achieved 56.05% overall accuracy. |
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- ACEBench: Achieved 60.90% summary score, demonstrating superior generalization and robustness. |
