unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF

Qwen

- Fine-tune Qwen3 (14B) for free using our Google [Colab notebook](https://docs.unsloth.ai/get-started/unsloth-notebooks)! - Read our Blog about Qwen3 support: [unsloth.ai/blog/qwen3](https://unsloth.ai/blog/qwen3) - View the rest of our notebooks in our [docs here](https://docs.unsloth.ai/get-started/unsloth-notebooks). **Qwen3-Coder** is available in multiple sizes. Today, we're excited to introduce **Qwen3-Coder-30B-A3B-Instruct**. This streamlined model maintains impressive performance and efficiency, featuring the following key enhancements: - **Significant Performance** among open models on **Agentic Coding**, **Agentic Browser-Use**, and other foundational coding tasks. - **Long-context Capabilities** with native support for **256K** tokens, extendable up to **1M** tokens using Yarn, optimized for repository-scale understanding. - **Agentic Coding** supporting for most platform such as **Qwen Code**, **CLINE**, featuring a specially designed function call format. **Qwen3-Coder-30B-A3B-Instruct** has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 30.5B in total and 3.3B activated - Number of Layers: 48 - Number of Attention Heads (GQA): 32 for Q and 4 for KV - Number of Experts: 128 - Number of Activated Experts: 8 - Context Length: **262,144 natively**. **NOTE: This model supports only non-thinking mode and does not generate ``<think></think>`` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.** For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3-coder/), [GitHub](https://github.com/QwenLM/Qwen3-Coder), and [Documentation](https://qwen.readthedocs.io/en/latest/). We advise you to use the latest version of `transformers`. With `transformers<4.51.0`, you will encounter the following error: KeyError: 'qwen3_moe' The following contains a code snippet illustrating how to use the model generate content based on given inputs. from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-Coder-30B-A3B-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) prompt = "Write a quick sort algorithm." messages = [ {"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=65536 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() content = tokenizer.decode(output_ids, skip_special_tokens=True) print("content:", content) **Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768`.** For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3. Qwen3-Coder excels in tool calling capabilities. You can simply define or use any tools as following example. def square_the_number(num: float) -> dict: return num ** 2 tools=[ { "type":"function", "function":{ "name": "square_the_number", "description": "output the square of the number.", "parameters": { "type": "object", "required": ["input_num"], "properties": { 'input_num': { 'type': 'number', 'description': 'input_num is a number that will be squared' } }, } } } ] import OpenAI client = OpenAI( base_url='http://localhost:8000/v1', # api_base api_key="EMPTY" ) messages = [{'role': 'user', 'content': 'square the number 1024'}] completion = client.chat.completions.create( messages=messages, model="Qwen3-Coder-30B-A3B-Instruct", max_tokens=65536, tools=tools, ) print(completion.choice[0]) To achieve optimal performance, we recommend the following settings: 1. **Sampling Parameters**: - We suggest using `temperature=0.7`, `top_p=0.8`, `top_k=20`, `repetition_penalty=1.05`. 2. **Adequate Output Length**: We recommend using an output length of 65,536 tokens for most queries, which is adequate for instruct models. If you find our work helpful, feel free to give us a cite. @misc{qwen3technicalreport, title={Qwen3 Technical Report}, author={Qwen Team}, year={2025}, eprint={2505.09388}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.09388}, }

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