• Device Model: A06

• Manufacturer: Samsung

• Architecture: aarch64

Resource Type

• Kernel Tree

Description

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Guides can be rendered in HTML or PDF. Please read Documentation/admin-guide/README.rst first.

Formatted documentation is also available online: Kernel Documentation

Refer to Documentation/process/changes.rst for build requirements and known issues.

Source / Link

• GitHub - TeamWin/android_kernel_samsung_a06 · GitHub

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- **Manufacturer:** [e.g., Google]
- **Architecture:** [e.g., ARM64]

### Resource Type
- Kernel Tree / Vendor Blobs / Device Tree / HAL / Other

### Description
- [Brief description of what this resource contains]

### Source / Link
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### Additional Notes
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I needed a custom AI model that was:

• 100% open-source and MIT-licensed, so I could use it in my own commercial projects without any legal headaches.
• Incredibly lightweight, so it could run on standard, cheap hardware, like a laptop with just 2GB of VRAM or a basic web server with 512MB of RAM.

It sounded impossible, but I actually managed to do it in an afternoon. I built my own AI, trained it to chat exactly how I wanted, and shrunk the final “brain” down to a tiny 89MB file that runs completely offline.

If you know a little bit of Python, you can do this too. Here is the exact, step-by-step process I used.

Phase 1: Creating the Training Data

You can’t train an AI without data. If you want your model to chat casually, write code, or parse logs, you have to show it exactly what that looks like.

Instead of typing out thousands of examples by hand, I used a larger, smarter AI (I used a free model called Trinity Large via OpenRouter) to act as a “Teacher.” I wrote a Python script that asked the Teacher to generate 2,000 different examples of the behavior I wanted.

Tip for speed: To make the script fast, I used threading so it would generate 20 examples at the exact same time.

Here is the script I wrote to generate the data:

import os
import json
import requests
import concurrent.futures
import threading

OPENROUTER_API_KEY = "YOUR_API_KEY"
MODEL_ID = "arcee-ai/trinity-large-preview:free"

counter_lock = threading.Lock()
file_lock = threading.Lock()
success_count = 0
total_samples = 2000

def generate_sample(category):
    headers = {
        "Authorization": f"Bearer {OPENROUTER_API_KEY}",
        "Content-Type": "application/json"
    }

    # Custom prompts based on what we want the AI to learn
    if category == "saas":
        system_prompt = "Generate a realistic SaaS operation interaction (e.g., log parsing, JSON output)."
    elif category == "coding":
        system_prompt = "Generate a realistic programming or tech-support interaction."
    else:
        system_prompt = "Generate a realistic, short casual conversation."

    data = {
        "model": MODEL_ID,
        "messages": [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": "Generate one example interaction."}
        ],
        "response_format": {"type": "json_object"}
    }
    
    try:
        response = requests.post("https://openrouter.ai/api/v1/chat/completions", headers=headers, json=data)
        response.raise_for_status()
        content = json.loads(response.json()['choices'][0]['message']['content'])
        
        # We format this using "ChatML" tags. 
        # This teaches the AI exactly when a user stops talking and the AI should start.
        chatml = "<|im_start|>system\nYou are an efficient, lightweight AI assistant.<|im_end|>\n"
        chatml += f"<|im_start|>user\n{content.get('user', '')}<|im_end|>\n"
        chatml += f"<|im_start|>assistant\n{content.get('assistant', '')}<|im_end|>"
        
        return {"text": chatml}
    except Exception:
        return None

def worker(category, output_file):
    global success_count
    sample = generate_sample(category)
    if sample:
        with file_lock:
            with open(output_file, "a", encoding="utf-8") as f:
                f.write(json.dumps(sample) + "\n")
        with counter_lock:
            success_count += 1
            if success_count % 10 == 0:
                print(f"Progress: {success_count}/{total_samples} generated...")

def main():
    print("Beginning data generation...")
    categories = ["saas", "general", "coding", "creative"]
    
    with concurrent.futures.ThreadPoolExecutor(max_workers=20) as executor:
        futures = []
        for i in range(total_samples):
            # Rotate through the categories so we get a balanced dataset
            futures.append(executor.submit(worker, categories[i % 4], "training_data.jsonl"))
            
        for future in concurrent.futures.as_completed(futures):
            pass

if __name__ == "__main__":
    main()

If you run this, you will end up with a file called training_data.jsonl packed with perfectly formatted examples.

Phase 2: Teaching Your Model (Fine-Tuning)

For the foundation of my AI, I chose OpenAI’s GPT-2 (124M). It’s an older model, but it is completely open-source and extremely small. By itself, GPT-2 isn’t very smart. But when we expose it to our new high-quality dataset, it learns our specific patterns.

I didn’t have a giant graphics card to train this. I actually trained it on a normal pc using the CPU.

To prevent memory crashes, we use a technique called LoRA (Low-Rank Adaptation). LoRA freezes 99% of the model and only trains a tiny, specific fraction of the parameters. This allows everyday computers to train AI.

Here is the training script. You will need to pip install torch transformers datasets peft to run it.

import torch
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer, DataCollatorForLanguageModeling
from peft import LoraConfig, get_peft_model

def main():
    model_id = "openai-community/gpt2"
    
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    tokenizer.pad_token = tokenizer.eos_token
    
    # Load model on CPU. 
    # If you have an NVIDIA GPU, change this to device_map="auto" and torch_dtype=torch.float16
    model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cpu", torch_dtype=torch.float32)

    # Setup LoRA to dramatically lower RAM usage during training
    lora_config = LoraConfig(
        r=8,
        lora_alpha=16,
        target_modules=["c_attn"],
        task_type="CAUSAL_LM"
    )
    model = get_peft_model(model, lora_config)
    
    dataset = load_dataset("json", data_files="training_data.jsonl", split="train")
    
    def tokenize_func(examples):
        return tokenizer(examples["text"], padding="max_length", truncation=True, max_length=256)
        
    tokenized_dataset = dataset.map(tokenize_func, batched=True, remove_columns=["text"])
    
    # We use a batch size of 1 so it doesn't crash low-end hardware
    training_args = TrainingArguments(
        output_dir="./custom-model",
        per_device_train_batch_size=1,     
        gradient_accumulation_steps=8,    
        learning_rate=2e-4,
        max_steps=200, # CPU training is slow, so 200 steps is a good start
        fp16=False,                        
        optim="adamw_torch",
        report_to="none"
    )
    
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_dataset,
        data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False)
    )
    
    print("Training the brain...")
    trainer.train()
    
    # Save the custom brain adapter!
    model.save_pretrained("./my-lora-adapter")
    tokenizer.save_pretrained("./my-lora-adapter")

if __name__ == "__main__":
    main()

Let this run. Depending on your computer, it might take a little while. Once it finishes, you’ll have a folder called my-lora-adapter containing everything your AI just learned.

Phase 3: Shrinking the Model (Quantization)

Our trained model works, but it takes up around 250MB. I wanted this to run on basic web servers that only have 512MB of total RAM. To make it fit, we need to “Quantize” it.

Quantization is the process of compressing the math inside the neural network from highly precise 16-bit decimals down to chunky 4-bit integers. It drastically shrinks the file size, but the AI barely loses any intelligence.

1. Merge the Model

First, we need to permanently fuse our newly trained LoRA adapter from Phase 2 into the original GPT-2 model:

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load the blank base model
base_model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2", device_map="cpu")

# Load our custom brain
model = PeftModel.from_pretrained(base_model, "./my-lora-adapter")

# Merge them permanently into a single model
model = model.merge_and_unload()
model.save_pretrained("./fully-merged-model")

2. Compress the File

Next, to actually compress it, you need to use an incredible open-source C++ project called llama.cpp.
When you download llama.cpp, it comes with a Python script to convert your model, and an executable program to shrink it.

Run these two commands in your terminal:

# 1. Convert our merged model to the standard GGUF format
python convert_hf_to_gguf.py ./fully-merged-model --outfile my-ai-f16.gguf

# 2. Compress the file down to pure 4-bit using the Q4_K_M algorithm
./llama-quantize my-ai-f16.gguf final-model.gguf Q4_K_M

The result? The model shrinks from 250MB down to a staggering 89 Megabytes.

Phase 4: Using Your AI (Connecting the API)

You now have final-model.gguf. This tiny file contains your custom, offline AI.

To actually chat with it, you use a program that comes bundled with llama.cpp called llama-server. You just run it in your terminal like this:

./llama-server -m final-model.gguf -c 256 -np 2 --host 127.0.0.1 --port 8080 -cb -mmap

Important: The -c 256 flag restricts the memory so it never crashes, and -np 2 tells it to only use 2 CPU cores.

Now the AI is running locally on your machine on port 8080. If you want to connect a website or an app to it, you just send an HTTP request to that port.

For example, here is a very simple PHP script that you could put on your web server. It takes a message from a user, formats it exactly how we trained the AI in Phase 1, and returns the response:

<?php
$input = json_decode(file_get_contents('php://input'), true);
$user_msg = $input['message'] ?? 'Hello';

// Format the prompt EXACTLY like our training data from Phase 1
$prompt = "<|im_start|>system\nYou are an efficient AI.<|im_end|>\n";
$prompt .= "<|im_start|>user\n" . $user_msg . "<|im_end|>\n<|im_start|>assistant\n";

$payload = json_encode([
    'prompt' => $prompt,
    'n_predict' => 128, 
    'stop' => ["<|im_end|>"]
]);

// Send the prompt to our offline AI server
$ch = curl_init('http://127.0.0.1:8080/completion');
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, $payload);

$response = json_decode(curl_exec($ch), true);
curl_close($ch);

// Output the AI's reply
echo json_encode(['reply' => trim($response['content'])]);
?>

Final Thoughts

And just like that, you’ve built a fully functional, offline AI. When you chat with it, the response flies over the local server instantly—powered entirely by an 89MB file that behaves exactly the way you trained it to.

If you’ve ever felt intimidated by AI development, just know it doesn’t take supercomputers anymore. It just takes an afternoon, some basic Python skills, and a little bit of patience.

Have questions about building your AI model?
Comment below and I’ll help you out!

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I am glad to say that ArachnoScript which had its 1st public release on January 3rd of 2026 (v0.1), is about to get its 2nd release v0.2.

ArachnoScript is a backend focused language that inherits syntax and semantics from JavaScript.

The History of ArachnoScript

ArachnoScript was initially intended to be “another” frontend UI library similar to react but without all the ‘horror’ as I may have called it.

The library was to be called Verdex (Virtual DOM EXtension), but some how I spelt it with an ‘e’ instead of ‘i’ (Virdex). Later on I wanted to make it a full-fledged language as building a UI library like react was insane and impossible (to understand).

As I went deeper into language design, I forgot about the UI library. Soon I decided to rename the language, that was when ArachnoScript was born. My reason for the name will remain untold until some time in future, when I resume this other project that I abandoned, ‘WebOS’.

ArachnoScript shares a lot of similarities with JavaScript primarily because JS is my favorite language, and also so as to maintain familiarity for web developers trying to build a web app with the UI library Verdex.

There are so many similarities, and so are there many differences.

ArachnoScript was built all the way from scratch with its own parser and runtime. v0.1 and v0.2 only let AS exist as an interpreted language and based on benchmarks that I made myself, AS is comparatively about 1,000,000 times slower than JS.

Although, v0.3 will be coming with the VM (Virtual Machine) for AS. The VM will significantly improve speed as its compiler will do optimizations and there will be less overhead at runtime.

ArachnoScript Features

• ArachnoScript is focused mainly on backend development.

• It currently has an experimental UI library for web apps as of v0.1. v0.2 promises a more stable and feature rich UI library.

• It currently supports async / await. v0.2 promises service workers and even coroutines.

• ArachnoScript supports many of JavaScript’s syntax with a touch of its own.

• ArachnoScript has about 13 data types.

• ArachnoScript supports JavaScript’s single line comments and its very own doc comments that start with ‘$’ and end at the end of the line or with another dollar sign.

• v0.2 offers a more powerful standard library for AS with better error messages.

• AS’s built-in functions are called ‘macros’ and it is packed with a tonne of macros.

Macros are regarded as unsafe and are meant to stay in the standard library.

• AS does not allow prototype modification.

• Arrays, classes, instances, and objects are of different data types in AS.

• AS of v0.1, the private and public keywords were no ops or had no effect on class members. But v0.2 will deliver concrete member accessibilty.

• AS fully supports inheritance.

• AS aims for cross platform compatibility.

• v0.1 ships with a weak and premature REPL, v0.2 might not include a REPL at all but if it does, then it will be of low priority being the of the last few features that will be implemented in v0.2 before its release.

ArachnoScript Syntax

Variable Declarations:

Mutable variables are declared with the ‘spawn’ keyword.

spawn name = “ECMA King”;

Constants are with ‘immortal’ in front.

immortal spawn name = “ECMA King”;

Mutable and local scoped.

var a = 0;

Control Flow:

// if-else block

if (true) {}

else if (false) {}

else true;

// switch cases

switch (true) {

case true: break;

default:

}

// match expressions

match (100) {

100 => true,

}

Loops:

// traditional for loop

for (i = 0; i < 10; i++) {}

// while loops

while (true) { break; }

do { break; } while (true);

// for..in loops

for (immortal spawn key in object) {}

// for..of loops

for (immortal spawn value of object) {}

It’s all the same for function statements, function expressions, and arrow functions.

Comments:

// line comment

$ doc comment

$ end with another dollar sign $

$ or at end of line

globalThis:

globalThis is a keyword that is used as an identifier in member expressions to access all variables, and other declarations at the top level of the current script.

var obj = {};

globalThis.obj; // {}

globalThis.obj = 0;

obj; // 0

globalThis.abc; // undefined

var abc = 123;

globalThis.abc; // 123

Type system

AS is dynamically typed like JavaScript. But soon AS will get its own compile time type system allowing for optional static typing.

Macros

Macros are built-ins that enable AS carry out operations that cannot be simply defined in the language.

// arrays created with the literal syntax

// are not objects

var array = [1, 2, 4];

// returns the length of an AS array

// you won’t have to use this macro as it would have been wrapped with another function defined in the standard library

#_array_length(array); // 3

// a simple print function that prints strings and only strings

function print(string) {

// if the value passed is not a string or is not convertible

// to a byte array, then this macro will throw an error

spawn byte_array = #_new_byte_array(string);

// Macros are usually built-in functions

// but there are built-in variables like #_os_stdout too

// #_file_write writes the data of its 2nd argument

// to the file passed as its 1st argument

#_file_write(#_os_stdout, byte_array)

}

// As from v0.2, escapes work!

print(“Hello World! from \\x1b\[1;97mAS\\x1b\[0m\\r\\n”)

\`\`\`

```js

// a #_new_byte_array returns a value of type raw \[byte array\]

spawn byte_array = #_new_byte_array()

// AS operators and keywords cannot carry operations on raw values.

// only macros can.

I Maintain an open source project called crashvault: GitHub - Ak-dude/crashvault: Crashvault is a lightweight, local-first crash/error vault with a simple CLI. Log errors, group them into issues, search, export/import, and keep a local history

Crashvault is a lightweight, local-first crash/error vault with a simple CLI. Log errors, group them into issues, search, export/import, and keep a local history

github: GitHub - Ak-dude/crashvault: Crashvault is a lightweight, local-first crash/error vault with a simple CLI. Log errors, group them into issues, search, export/import, and keep a local history

Reasons why YOU would want to use AS

I’ve heard the saying “Devs really love to hate on JavaScript“.

I wondered why devs hate on javascript, JavaScript was actually my favorite language.

Until I one day someone told me why.

• prototype modification
• Multiple ways to do similar things with different performance characteristics
• The web framework family around it
• dynamic typed nature
• and some other disturbing things…

I could not argue. But AS will come as the cousin of JS to resolve many of these pains.

AS is a backend focused language with a built-in library called Verdex that makes AS supports frontend web dev, and GUIs for mobile and desktop. And it does not allow for prototype modification as prototypes are not a simple object property or member.

Verdex uses AS + HTML (ASX) to define layouts. But the GUI library is very limited and experimental.

One great feature that I will soon implement is the compile time type checking and built-in debugger. But the debugger may not be as equipped as the ones that come with your favorite IDE.

AS also has similar but diverse types

• number
• string
• array [not objects]
• object
• class
• instance
• function
• macro
• raw
• symbol
• boolean
• undefined
• null

This makes it easier to differentiate between value types

AS currently supports async/await, soon service workers

and also coroutines

AS will support most JavaScript syntax and also adding a few useful ones like match expressions which are currently supported

More about AS later. I have released v0.1, but its repo is not clean and may not be easy to use. But you can study the standard library and get to learn the way of AS.

So that until I release v0.2 packed with cleaner and faster runtime and environment

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Here is the link to the github repo: GitHub - ECMA-King87/ArachnoScript-Programming-Language: A JavaScript-inspired programming language with a custom runtime and web tooling.

You can also find some info about it by looking it up on google or other search engines

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