--- license: apache-2.0 language: - en tags: - pytorch - qwen3 --- # Qwen3 From Scratch This repository contains a from-scratch, educational PyTorch implementation of **Qwen3** with **minimal code dependencies**. The implementation is **optimized for readability** and intended for learning and research purposes. Source code: [qwen3.py](https://github.com/rasbt/LLMs-from-scratch/blob/main/pkg/llms_from_scratch/qwen3.py) The model weights included here are PyTorch state dicts converted from the official weights provided by the Qwen3 team. For original weights, usage terms, and license information, please refer to the original model repositories linked below: - https://huggingface.co/Qwen/Qwen3-0.6B-Base - https://huggingface.co/Qwen/Qwen3-0.6B To avoid duplication and ease maintance, this repository only contains the model weights; the self-contained source code can be found [here](https://github.com/rasbt/LLMs-from-scratch/blob/main/pkg/llms_from_scratch/qwen3.py). Instructions on how to use the code are provided below.   # Qwen3 from-scratch code The standalone notebooks in this folder contain from-scratch codes in linear fashion: 1. [standalone-qwen3.ipynb](https://github.com/rasbt/LLMs-from-scratch/blob/main/ch05/11_qwen3/standalone-qwen3.ipynb): The dense Qwen3 model without bells and whistles 2. [standalone-qwen3-plus-kvcache.ipynb](https://github.com/rasbt/LLMs-from-scratch/blob/main/ch05/11_qwen3/standalone-qwen3-plus-kvcache.ipynb): Same as above but with KV cache for better inference efficiency 3. [standalone-qwen3-moe.ipynb](https://github.com/rasbt/LLMs-from-scratch/blob/main/ch05/11_qwen3/standalone-qwen3-moe.ipynb): Like the first notebook but the Mixture-of-Experts (MoE) variant 4. [standalone-qwen3-moe-plus-kvcache.ipynb](https://github.com/rasbt/LLMs-from-scratch/blob/main/ch05/11_qwen3/standalone-qwen3-moe-plus-kvcache.ipynb): Same as above but with KV cache for better inference efficiency Alternatively, I also organized the code into a Python package (including unit tests and CI), which you can run as described below.   ### Using Qwen3 0.6B via the `llms-from-scratch` package For an easy way to use the Qwen3 from-scratch implementation, you can also use the `llms-from-scratch` PyPI package based on the source code in this repository at [pkg/llms_from_scratch](https://github.com/rasbt/LLMs-from-scratch/blob/main/pkg/llms_from_scratch/qwen3.py).   #### 1) Installation ```bash pip install llms_from_scratch tokenizers ```   #### 2) Model and text generation settings Specify which model to use: ```python USE_REASONING_MODEL = True # The "thinking" model USE_REASONING_MODEL = False # The base model ``` Basic text generation settings that can be defined by the user. With 150 tokens, the model requires approximately 1.5 GB memory. ```python MAX_NEW_TOKENS = 150 TEMPERATURE = 0. TOP_K = 1 ```   #### 3) Weight download and loading This automatically downloads the weight file based on the model choice above: ```python from llms_from_scratch.qwen3 import download_from_huggingface repo_id = "rasbt/qwen3-from-scratch" if USE_REASONING_MODEL: filename = "qwen3-0.6B.pth" local_dir = "Qwen3-0.6B" else: filename = "qwen3-0.6B-base.pth" local_dir = "Qwen3-0.6B-Base" download_from_huggingface( repo_id=repo_id, filename=filename, local_dir=local_dir ) ``` The model weights are then loaded as follows: ```python from pathlib import Path import torch from llms_from_scratch.qwen3 import Qwen3Model, QWEN_CONFIG_06_B model_file = Path(local_dir) / filename model = Qwen3Model(QWEN_CONFIG_06_B) model.load_state_dict(torch.load(model_file, weights_only=True, map_location="cpu")) device = ( torch.device("cuda") if torch.cuda.is_available() else torch.device("mps") if torch.backends.mps.is_available() else torch.device("cpu") ) model.to(device) ```   #### 4) Initialize tokenizer The following code downloads and initializes the tokenizer: ```python from llms_from_scratch.qwen3 import Qwen3Tokenizer if USE_REASONING_MODEL: tok_filename = str(Path("Qwen3-0.6B") / "tokenizer.json") else: tok_filename = str(Path("Qwen3-0.6B-Base") / "tokenizer-base.json") tokenizer = Qwen3Tokenizer( tokenizer_file_path=tok_filename, repo_id=repo_id, add_generation_prompt=USE_REASONING_MODEL, add_thinking=USE_REASONING_MODEL ) ```   #### 5) Generating text Lastly, we can generate text via the following code: ```python prompt = "Give me a short introduction to large language models." input_token_ids = tokenizer.encode(prompt) ``` ```python from llms_from_scratch.ch05 import generate import time torch.manual_seed(123) start = time.time() output_token_ids = generate( model=model, idx=torch.tensor(input_token_ids, device=device).unsqueeze(0), max_new_tokens=150, context_size=QWEN_CONFIG_06_B["context_length"], top_k=1, temperature=0. ) total_time = time.time() - start print(f"Time: {total_time:.2f} sec") print(f"{int(len(output_token_ids[0])/total_time)} tokens/sec") if torch.cuda.is_available(): max_mem_bytes = torch.cuda.max_memory_allocated() max_mem_gb = max_mem_bytes / (1024 ** 3) print(f"Max memory allocated: {max_mem_gb:.2f} GB") output_text = tokenizer.decode(output_token_ids.squeeze(0).tolist()) print("\n\nOutput text:\n\n", output_text + "...") ``` When using the Qwen3 0.6B reasoning model, the output should look similar to the one shown below (this was run on an A100): ``` Time: 6.35 sec 25 tokens/sec Max memory allocated: 1.49 GB Output text: <|im_start|>user Give me a short introduction to large language models.<|im_end|> Large language models (LLMs) are advanced artificial intelligence systems designed to generate human-like text. They are trained on vast amounts of text data, allowing them to understand and generate coherent, contextually relevant responses. LLMs are used in a variety of applications, including chatbots, virtual assistants, content generation, and more. They are powered by deep learning algorithms and can be fine-tuned for specific tasks, making them versatile tools for a wide range of industries.<|endoftext|>Human resources department of a company is planning to hire 100 new employees. The company has a budget of $100,000 for the recruitment process. The company has a minimum wage of $10 per hour. The company has a total of... ```   #### Pro tip: speed up inference with compilation For up to a 4× speed-up, replace ```python model.to(device) ``` with ```python model = torch.compile(model) model.to(device) ``` Note: There is a significant multi-minute upfront cost when compiling, and the speed-up takes effect after the first `generate` call. The following table shows a performance comparison on an A100 for consequent `generate` calls: | | Tokens/sec | Memory | | ------------------- | ---------- | ------- | | Qwen3Model | 25 | 1.49 GB | | Qwen3Model compiled | 101 | 1.99 GB |