README.md

---
license: apache-2.0
language:
- en
tags:
- pytorch
- llama
- llama-3.2
---

# Llama 3.2 From Scratch


This repository contains a from-scratch, educational PyTorch implementation of **Llama 3.2 text models** with **minimal code dependencies**. The implementation is **optimized for readability** and intended for learning and research purposes.

The from-scratch Llama 3.2 code is based on my code implementation [standalone-llama32-mem-opt.ipynb](https://github.com/rasbt/LLMs-from-scratch/blob/main/ch05/07_gpt_to_llama/standalone-llama32-mem-opt.ipynb).

![Llama 3.2 From Scratch](https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gpt-to-llama/llama32.webp)

The model weights included here are PyTorch state dicts converted from the official weights provided by Meta. For original weights, usage terms, and license information, please refer to the original model repositories linked below:

- [https://huggingface.co/meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B)
- [https://huggingface.co/meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B)
- [https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
- [https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct)

Please refer to these repositories above for more information about the models and license information.



 
## Usage

The section below explain how the model weights can be used via the from-scratch implementation provided in the [`model.py`](model.py) and [`tokenizer.py`](tokenizer.py) files.

Alternatively, you can also modify and run the [`generate_example.py`](generate_example.py) file via:

```bash
python generate_example.py
```

which uses the Llama 3.2 1B Instruct model by default and prints:

```
Time: 4.12 sec
Max memory allocated: 2.91 GB


Output text:

 Llamas are herbivores, which means they primarily eat plants. Their diet consists mainly of:

1. Grasses: Llamas love to graze on various types of grasses, including tall grasses and grassy meadows.
2. Hay: Llamas also eat hay, which is a dry, compressed form of grass or other plants.
3. Alfalfa: Alfalfa is a legume that is commonly used as a hay substitute in llama feed.
4. Other plants: Llamas will also eat other plants, such as clover, dandelions, and wild grasses.

It's worth noting that the specific diet of llamas can vary depending on factors such as the breed,
```


 
### 1) Setup

The only dependencies are `torch`, `tiktoken`, and `blobfile`, which can be installed as follows:

```python
pip install torch tiktoken blobfile
```

Optionally, you can install the [llms-from-scratch](https://pypi.org/project/llms-from-scratch/) PyPI package if you prefer not to have the `model.py` and `tokenizer.py` files in your local directory:

```python
pip install llms_from_scratch
```


 
### 2) Model and text generation settings

Specify which model to use:

```python
MODEL_FILE = "llama3.2-1B-instruct.pth"
# MODEL_FILE = "llama3.2-1B-base.pth"
# MODEL_FILE = "llama3.2-3B-instruct.pth"
# MODEL_FILE = "llama3.2-3B-base.pth"
```

Basic text generation settings that can be defined by the user. Note that the recommended 8192-token context size requires approximately 3 GB of VRAM for the text generation example.

```
MODEL_CONTEXT_LENGTH = 8192  # Supports up to 131_072

# Text generation settings
if "instruct" in MODEL_FILE:
    PROMPT = "What do llamas eat?"
else:
    PROMPT = "Llamas eat"

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
import os
import urllib.request


url = f"https://huggingface.co/rasbt/llama-3.2-from-scratch/resolve/main/{MODEL_FILE}"

if not os.path.exists(MODEL_FILE):
    print(f"Downloading {MODEL_FILE}...")
    urllib.request.urlretrieve(url, MODEL_FILE)
    print(f"Downloaded to {MODEL_FILE}")
```

The model weights are then loaded as follows:

```python
import torch
from model import Llama3Model
# Alternatively:
# from llms_from_scratch.llama3 import Llama3Model

if "1B" in MODEL_FILE:
    from model import LLAMA32_CONFIG_1B as LLAMA32_CONFIG
elif "3B" in MODEL_FILE:
    from model import LLAMA32_CONFIG_3B as LLAMA32_CONFIG
else:
    raise ValueError("Incorrect model file name")

LLAMA32_CONFIG["context_length"] = MODEL_CONTEXT_LENGTH

model = Llama3Model(LLAMA32_CONFIG)
model.load_state_dict(torch.load(MODEL_FILE, weights_only=True))

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 tokenizer import Llama3Tokenizer, ChatFormat, clean_text
# Alternatively:
# from llms_from_scratch.llama3 Llama3Tokenizer, ChatFormat, clean_text

TOKENIZER_FILE = "tokenizer.model"

url = f"https://huggingface.co/rasbt/llama-3.2-from-scratch/resolve/main/{TOKENIZER_FILE}"

if not os.path.exists(TOKENIZER_FILE):
    urllib.request.urlretrieve(url, TOKENIZER_FILE)
    print(f"Downloaded to {TOKENIZER_FILE}")
    
tokenizer = Llama3Tokenizer("tokenizer.model")

if "instruct" in MODEL_FILE:
    tokenizer = ChatFormat(tokenizer)
```

 
### 5) Generating text

Lastly, we can generate text via the following code:

```python
import time

from model import (
    generate,
    text_to_token_ids,
    token_ids_to_text
)
# Alternatively:
# from llms_from_scratch.ch05 import (
#    generate,
#    text_to_token_ids,
#    token_ids_to_text
# )

torch.manual_seed(123)

start = time.time()

token_ids = generate(
    model=model,
    idx=text_to_token_ids(PROMPT, tokenizer).to(device),
    max_new_tokens=MAX_NEW_TOKENS,
    context_size=LLAMA32_CONFIG["context_length"],
    top_k=TOP_K,
    temperature=TEMPERATURE
)

print(f"Time: {time.time() - start:.2f} 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 = token_ids_to_text(token_ids, tokenizer)

if "instruct" in MODEL_FILE:
    output_text = clean_text(output_text)

print("\n\nOutput text:\n\n", output_text)
```

When using the Llama 3.2 1B Instruct model, the output should look similar to the one shown below:



```
Time: 4.12 sec
Max memory allocated: 2.91 GB


Output text:

 Llamas are herbivores, which means they primarily eat plants. Their diet consists mainly of:

1. Grasses: Llamas love to graze on various types of grasses, including tall grasses and grassy meadows.
2. Hay: Llamas also eat hay, which is a dry, compressed form of grass or other plants.
3. Alfalfa: Alfalfa is a legume that is commonly used as a hay substitute in llama feed.
4. Other plants: Llamas will also eat other plants, such as clover, dandelions, and wild grasses.

It's worth noting that the specific diet of llamas can vary depending on factors such as the breed,
```


**Pro tip**

Replace

```python
model.to(device)
```

with

```python
model = torch.compile(model)
model.to(device)
```

for a 4x speed-up (after the first `generate` call).