### 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](../../pkg/llms_from_scratch).
#### 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
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 2: speed up inference with compilation
You can significantly boost inference performance using the KV cache `Qwen3Model` drop-in replacement when running the model on a CPU. (See my [Understanding and Coding the KV Cache in LLMs from Scratch](https://magazine.sebastianraschka.com/p/coding-the-kv-cache-in-llms) article to learn more about KV caches.)
```python
from llms_from_scratch.kv_cache.qwen3 import Qwen3Model
from llms_from_scratch.kv_cache.generate import generate_text_simple
Note that the peak memory usage is only listed for Nvidia CUDA devices, as it is easier to calculate. However, the memory usage on other devices is likely similar as it uses a similar precision format, and the KV cache storage dominates here for the generated 150-token text (however, different devices may implement matrix multiplication differently and may result in different peak memory requirements).
