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README.md

@@ -9,5 +9,207 @@ tags:
 
 # 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.
 
-In progress...
+Source code: [qwen3.py](https://github.com/rasbt/LLMs-from-scratch/blob/main/pkg/llms_from_scratch/qwen3.py)
+
+<img src="https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/qwen/qwen-overview.webp">
+
+
+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.
+
+&nbsp;
+
+### 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).
+
+&nbsp;
+
+#### 1) Installation
+
+```bash
+pip install llms_from_scratch tokenizers
+```
+
+&nbsp;
+
+#### 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
+```
+
+&nbsp;
+
+#### 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)
+```
+
+&nbsp;
+
+#### 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
+)
+```
+
+
+
+&nbsp;
+
+#### 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...
+```
+
+&nbsp;
+
+#### 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 |