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--- |
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base_model: mlx-community/Qwen2.5-1.5B-Instruct-8bit |
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language: |
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- en |
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license: apache-2.0 |
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license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE |
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pipeline_tag: text-generation |
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tags: |
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- chat |
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- mlx |
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- brainrot |
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library_name: mlx |
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datasets: |
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- shvn22k/brainrot-dataset |
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--- |
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# DuckyBlender/brainrot-translator-mlx-8bit |
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This model can effectively translate Brainrot to English and English to Brainrot, with the correct system prompt. Trained in exactly 1 hour on a 16 GB M1 Pro Macbook (8-core) using MLX. This model actually works, and is probably my first actually useful successful fine-tune. |
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I trained Qwen2.5 instead of Qwen3, for simplicity because I had issues with Qwen3, probably because of the thinking modes. |
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## Information |
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This model [DuckyBlender/brainrot-translator-mlx-8bit](https://huggingface.co/DuckyBlender/brainrot-translator-mlx-8bit) was |
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converted to MLX format from [mlx-community/Qwen2.5-1.5B-Instruct-8bit](https://huggingface.co/mlx-community/Qwen2.5-1.5B-Instruct-8bit) |
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using mlx-lm version **0.25.2**. |
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## Training |
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```bash |
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mlx_lm.lora --model "mlx-community/Qwen2.5-3B-8bit" --data data --iters 2500 --max-seq-length 200 --num-layers 16 --batch-size 8 --save-every 25 --wandb diegogpt --train |
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``` |
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Peak memory: 6.2GB |
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Trained tokens: around 1.1M |
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Dataset is 11326 lines, so trained for ~16 epochs. |
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Calculation: |
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Total samples seen = iters × batch size = 11326 × 16 = 181216 |
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Epochs = total samples ÷ dataset size = 181216 ÷ 11326 ≈ 16 |
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I've tested the 200th iteration (around 1.5epoch) and it worked similarly well, but here I gave the 2500th iteration. |
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## Charts |
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## Use with mlx |
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```bash |
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pip install mlx-lm |
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``` |
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```python |
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import mlx.core as mx |
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from mlx_lm import load, generate |
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from mlx_lm.sample_utils import make_sampler |
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def main(): |
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print("🧠 Brainrot Translator") |
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print("=" * 30) |
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# Ask user for translation direction |
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print("Choose translation direction:") |
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print("1. English to Brainrot") |
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print("2. Brainrot to English") |
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while True: |
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choice = input("Enter choice (1 or 2): ").strip() |
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if choice in ["1", "2"]: |
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break |
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print("Invalid choice. Please enter 1 or 2.") |
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# Set system prompt based on choice |
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if choice == "1": |
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system_prompt = "Translate from English to Brainrot" |
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direction = "English → Brainrot" |
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else: |
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system_prompt = "Translate from Brainrot to English" |
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direction = "Brainrot → English" |
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print(f"\nMode: {direction}") |
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print("Loading model...") |
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# Load the model |
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model, tokenizer = load("DuckyBlender/brainrot-translator-mlx-8bit") |
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# model, tokenizer = load("mlx-community/Qwen2.5-1.5B-Instruct-8bit", adapter_path="adapters") |
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print("Model loaded! Enter text to translate (Ctrl+C to exit)") |
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print("-" * 50) |
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try: |
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while True: |
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# Get user input |
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user_input = input(f"\n[{direction.split(' → ')[0]}]: ").strip() |
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if not user_input: |
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continue |
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# Format the prompt |
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messages = [ |
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{"role": "system", "content": system_prompt}, |
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{"role": "user", "content": user_input} |
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] |
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# Apply chat template |
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prompt = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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sampler = make_sampler(temp=0.5, top_p=0.95) |
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print(f"[{direction.split(' → ')[1]}]: ", end="", flush=True) |
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# Generate response |
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response = generate( |
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model, |
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tokenizer, |
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prompt=prompt, |
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sampler=sampler, |
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max_tokens=128, |
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verbose=False |
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) |
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print(response) |
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except KeyboardInterrupt: |
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print("\n\nGoodbye! 👋") |
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if __name__ == "__main__": |
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main() |
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``` |
