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Initial release: Yuuki training pipeline

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Apache License
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Copyright 2026 OpceanAI
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@@ -63,130 +65,64 @@
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APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
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<div align="center">
# Yuuki Training Code
**Official training pipeline for Yuuki, an experimental small-scale language model for source code generation.**
[![Model](https://img.shields.io/badge/HuggingFace-Yuuki--82M-yellow)](https://huggingface.co/OpceanAI/Yuuki-82M)
[![License](https://img.shields.io/badge/License-Apache_2.0-blue)](LICENSE)
[![Python](https://img.shields.io/badge/Python-3.8+-green)](https://python.org)
</div>
---
## Abstract
This repository contains the official training implementation for **Yuuki**, a compact causal language model optimized for source code understanding and generation. The system is designed with an emphasis on simplicity, reproducibility, and accessibility across heterogeneous computing environments, including CPU-only systems, cloud notebooks (Colab, Kaggle), and resource-constrained platforms such as Termux on mobile devices.
---
## Model Specification
| Attribute | Description |
|-----------|-------------|
| **Architecture** | GPT-style autoregressive transformer |
| **Base Model** | `distilgpt2` |
| **Domain** | Source code (multi-language) |
| **Training Corpus** | `bigcode/the-stack-smol-xl` |
| **Parameter Count** | ~82M |
| **Design Principles** | Minimal dependencies, transparent implementation, full reproducibility |
---
## Repository Structure
### Included Components
| File | Description |
|------|-------------|
| `train_yuuki.py` | Complete, self-contained training script |
| `LICENSE` | Apache 2.0 License |
### Excluded Artifacts
The following components are intentionally omitted to maintain repository portability and encourage local reproducibility:
- Pre-trained model weights and checkpoints
- Tokenized datasets and Arrow cache files
- Training logs and metrics
- Experimental or proprietary scripts
- Auxiliary datasets from subsequent experiments
All artifacts should be generated locally by executing the provided training script.
---
## Configuration Parameters
Training behavior is controlled exclusively through environment variables, enabling seamless adaptation across diverse execution environments.
### Default Configuration
| Parameter | Default Value | Description |
|-----------|---------------|-------------|
| `MODEL_NAME` | `distilgpt2` | Pre-trained model identifier for initialization |
| `DATASET_ID` | `bigcode/the-stack-smol-xl` | HuggingFace dataset identifier |
| `SPLIT` | `train` | Dataset partition for training |
| `OUTPUT_DIR` | `./yuuki_model` | Output directory for model artifacts |
| `TOKENIZED_CACHE_DIR` | `./yuuki_model/tokenized_cache` | Cache location for tokenized sequences |
| `MAX_LENGTH` | `256` | Maximum input sequence length |
| `EPOCHS` | `2` | Number of training iterations |
| `BATCH_SIZE` | `1` | Samples per gradient update |
### Implementation Notes
- **Sequence Length (`MAX_LENGTH=256`)**: Selected to optimize memory utilization and training throughput on constrained hardware.
- **Batch Size (`BATCH_SIZE=1`)**: Configured for compatibility with low-memory execution environments.
- **Tokenization Caching**: Optional but recommended for iterative training workflows.
---
## Execution
### Standard Invocation
```bash
python train_yuuki.py
```
### Custom Configuration Example
```bash
MODEL_NAME=distilgpt2 \
MAX_LENGTH=256 \
EPOCHS=3 \
BATCH_SIZE=2 \
python train_yuuki.py
```
The training script performs automatic hardware detection and configures CUDA acceleration when available.
---
## Design Rationale
Yuuki is not intended to compete with large-scale foundation models. The project objectives are:
| Principle | Description |
|-----------|-------------|
| **Interpretability** | Prioritizes readable, maintainable code over abstraction layers |
| **Accessibility** | Executable without specialized hardware infrastructure |
| **Transparency** | No hidden procedures or undocumented dependencies |
| **Educational Utility** | Serves as a reference implementation for language model training |
---
## Pre-trained Model
The model trained using this pipeline is publicly available:
<div align="center">
**[Yuuki-82M on HuggingFace](https://huggingface.co/OpceanAI/Yuuki-82M)**
</div>
---
## Limitations and Disclaimer
This software is provided for research and educational purposes. The model may produce:
- Syntactically or semantically incorrect code
- Incomplete or truncated outputs
- Potentially unsafe or nonsensical suggestions
**This system is not suitable for production deployment.** Users assume full responsibility for any application of the generated outputs.
---
## License
This project is distributed under the **Apache License 2.0**. See the [LICENSE](LICENSE) file for complete terms.
Under this license, you are permitted to:
- Use, copy, and distribute the software
- Modify and create derivative works
- Use for commercial and non-commercial purposes
Subject to the conditions of attribution and license preservation as specified in the Apache 2.0 terms.
---
## Contact
For inquiries, collaboration proposals, or technical discussions regarding Yuuki, please submit an Issue or initiate a Discussion in this repository.
---
<div align="center">
**Developed by [OpceanAI](https://huggingface.co/OpceanAI)**
</div>

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#!/usr/bin/env python3
"""
train_yuuki_refined.py
Refined training script for Yuuki (mobile-first).
Features:
- Detects whether CUDA is available and adjusts some args.
- Skips tokenization if dataset already tokenized (caches arrows).
- ResourceMonitorCallback: autosaves when RAM or CPU thresholds hit.
- Autosave every SAVE_STEPS (default 500) and keeps only SAVE_TOTAL_LIMIT checkpoints.
- Pretty progress bar (tqdm) and lightweight logging.
- Graceful SIGINT handler that forces a manual save before exiting.
Designed for Termux / mobile but also works on desktop (it will auto-detect device).
Run: python train_yuuki_refined.py
Note: optional dependencies: psutil and tqdm. Install if missing: pip install psutil tqdm
"""
import os
import math
import signal
import logging
import sys
from pathlib import Path
# Optional imports
try:
import psutil
except Exception:
psutil = None
try:
from tqdm import tqdm
except Exception:
tqdm = None
from datasets import load_dataset
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
DataCollatorForLanguageModeling,
Trainer,
TrainingArguments,
TrainerCallback,
)
# -----------------------
# Config (tweak for your device)
# -----------------------
MODEL_NAME = os.environ.get("MODEL_NAME", "distilgpt2")
DATASET_ID = os.environ.get("DATASET_ID", "bigcode/the-stack-smol-xl")
SPLIT = os.environ.get("SPLIT", "train")
OUTPUT_DIR = os.environ.get("OUTPUT_DIR", "./yuuki_model")
TOKENIZED_CACHE_DIR = os.environ.get("TOKENIZED_CACHE_DIR", os.path.expanduser("~/yuuki/tokenized_cache"))
MAX_LENGTH = int(os.environ.get("MAX_LENGTH", "256")) # v0.1 - 4x más rápido que 512
EPOCHS = int(os.environ.get("EPOCHS", "2"))
BATCH_SIZE = int(os.environ.get("BATCH_SIZE", "1"))
GRADIENT_ACCUMULATION = int(os.environ.get("GRADIENT_ACCUMULATION", "4")) # Reducido de 8 a 4 para pasos más rápidos
# Effective batch will be printed at start: BATCH_SIZE * GRADIENT_ACCUMULATION
# Autosave frequent for mobile small steps
SAVE_STEPS = int(os.environ.get("SAVE_STEPS", "50")) # Guarda checkpoint cada 50 pasos (muy frecuente para móvil)
SAVE_TOTAL_LIMIT = int(os.environ.get("SAVE_TOTAL_LIMIT", "5")) # Mantiene solo los últimos 5 checkpoints (borra automáticamente los más antiguos)
LOGGING_STEPS = int(os.environ.get("LOGGING_STEPS", "10")) # Log cada 10 pasos
# Resources thresholds
CHECK_RESOURCES_EVERY_N_STEPS = int(os.environ.get("CHECK_RESOURCES_EVERY_N_STEPS", "50"))
MEMORY_THRESHOLD = float(os.environ.get("MEMORY_THRESHOLD", "0.12")) # fraction available
CPU_THRESHOLD = int(os.environ.get("CPU_THRESHOLD", "95"))
# Map batch for tokenization (reduce if memory issues)
MAP_BATCH_SIZE = int(os.environ.get("MAP_BATCH_SIZE", "128"))
# Safety limits (don't change unless you know what you do)
MIN_FREE_RAM_MB = 80 # try to keep at least this free RAM
# Setup logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s: %(message)s")
logger = logging.getLogger("train_yuuki")
# -----------------------
# Utility helpers
# -----------------------
def has_cuda():
try:
import torch
return torch.cuda.is_available()
except Exception:
return False
def human_size(num_bytes: int) -> str:
for unit in ['B', 'KB', 'MB', 'GB', 'TB']:
if abs(num_bytes) < 1024.0:
return f"{num_bytes:3.1f}{unit}"
num_bytes /= 1024.0
return f"{num_bytes:.1f}PB"
def get_last_checkpoint(output_dir):
"""Find the most recent checkpoint directory in output_dir."""
if not os.path.exists(output_dir):
return None
checkpoints = []
for item in os.listdir(output_dir):
item_path = os.path.join(output_dir, item)
if os.path.isdir(item_path) and item.startswith("checkpoint-"):
try:
step_num = int(item.split("-")[-1])
checkpoints.append((step_num, item_path))
except ValueError:
continue
if not checkpoints:
return None
# Return the checkpoint with the highest step number
checkpoints.sort(key=lambda x: x[0], reverse=True)
return checkpoints[0][1]
def cleanup_old_checkpoints(output_dir, max_checkpoints=5):
"""Ensure we never have more than max_checkpoints. Delete oldest ones if exceeded."""
if not os.path.exists(output_dir):
return
checkpoints = []
for item in os.listdir(output_dir):
item_path = os.path.join(output_dir, item)
if os.path.isdir(item_path) and item.startswith("checkpoint-"):
try:
step_num = int(item.split("-")[-1])
checkpoints.append((step_num, item_path))
except ValueError:
continue
# Sort by step number (newest first)
checkpoints.sort(key=lambda x: x[0], reverse=True)
# If we have more than max_checkpoints, delete the oldest ones
if len(checkpoints) > max_checkpoints:
to_delete = checkpoints[max_checkpoints:]
for step_num, checkpoint_path in to_delete:
logger.info(f"Eliminando checkpoint antiguo: {checkpoint_path}")
try:
import shutil
shutil.rmtree(checkpoint_path)
except Exception as e:
logger.warning(f"No se pudo eliminar {checkpoint_path}: {e}")
# -----------------------
# Callbacks
# -----------------------
class ResourceMonitorCallback(TrainerCallback):
"""Checks memory and CPU every N steps and requests a checkpoint save when thresholds are exceeded."""
def __init__(self, check_every_n=50, mem_threshold=0.12, cpu_threshold=95):
self.check_every_n = check_every_n
self.mem_threshold = mem_threshold
self.cpu_threshold = cpu_threshold
self._step = 0
self.psutil = psutil
def on_step_end(self, args, state, control, **kwargs):
self._step += 1
if self._step % self.check_every_n != 0:
return control
if self.psutil is None:
return control
try:
vm = self.psutil.virtual_memory()
avail_frac = vm.available / vm.total if vm.total else 1.0
cpu = int(self.psutil.cpu_percent(interval=None))
logger.debug(f"Resource check @ step {state.global_step}: avail_frac={avail_frac:.2f}, cpu={cpu}%")
if avail_frac < self.mem_threshold or cpu >= self.cpu_threshold:
logger.info(f"Resource threshold exceeded (mem={avail_frac:.2f}, cpu={cpu}%). Requesting save.")
control.should_save = True
control.should_log = True
except (PermissionError, OSError) as e:
# Termux may not have permissions to access /proc/stat
logger.debug(f"Cannot check resources (permission denied): {e}")
pass
return control
class TqdmProgressCallback(TrainerCallback):
"""Simple TQDM progress visualizer that prints loss when available.
Falls back to basic logging if tqdm is not installed.
"""
def __init__(self, total_steps=None):
self.total_steps = total_steps
self.pbar = None
self._last_log_step = 0
def on_train_begin(self, args, state, control, **kwargs):
if tqdm is None:
logger.info("tqdm not available — using default logs")
return
total = int(state.max_steps) if state.max_steps is not None and state.max_steps > 0 else self.total_steps
self.pbar = tqdm(total=total, desc="Training", unit="step")
def on_step_end(self, args, state, control, **kwargs):
if self.pbar:
# advance one step for each global step change
self.pbar.n = int(state.global_step)
# show approximate ETA and a minimal loss if logged
last = None
if hasattr(state, 'log_history') and state.log_history:
for e in reversed(state.log_history):
if 'loss' in e:
last = e['loss']
break
self.pbar.set_postfix({"loss": f"{last:.4f}" if last is not None else "-"})
self.pbar.refresh()
def on_train_end(self, args, state, control, **kwargs):
if self.pbar:
self.pbar.close()
class CheckpointCleanupCallback(TrainerCallback):
"""Cleans up old checkpoints after each save to maintain max limit."""
def __init__(self, output_dir, max_checkpoints=5):
self.output_dir = output_dir
self.max_checkpoints = max_checkpoints
def on_save(self, args, state, control, **kwargs):
"""Called after a checkpoint is saved."""
cleanup_old_checkpoints(self.output_dir, max_checkpoints=self.max_checkpoints)
return control
# -----------------------
# Signal handler for graceful save
# -----------------------
_save_requested = False
def _signal_handler(sig, frame):
global _save_requested
logger.warning("SIGINT received — will request a graceful save and stop after current step.")
_save_requested = True
signal.signal(signal.SIGINT, _signal_handler)
# -----------------------
# Main
# -----------------------
def main():
device = "cuda" if has_cuda() else "cpu"
logger.info(f"Device detected: {device}")
if device == "cpu":
logger.warning("⚠️ Entrenando en CPU - esto será MUY LENTO")
logger.warning("⚠️ Considera reducir MAX_LENGTH o usar un modelo más pequeño")
logger.warning("⚠️ Tiempo estimado: ~5 minutos por paso")
effective_batch = BATCH_SIZE * GRADIENT_ACCUMULATION
logger.info(f"Per-device batch_size={BATCH_SIZE}, gradient_accumulation_steps={GRADIENT_ACCUMULATION}, effective batch={effective_batch}")
# Create tokenized cache directory if it doesn't exist
os.makedirs(TOKENIZED_CACHE_DIR, exist_ok=True)
logger.info(f"Cache de tokenización: {TOKENIZED_CACHE_DIR}")
# Check for existing checkpoint to resume from
last_checkpoint = get_last_checkpoint(OUTPUT_DIR)
if last_checkpoint:
logger.info(f"¡Checkpoint encontrado! Reanudando desde: {last_checkpoint}")
else:
logger.info("No se encontró checkpoint previo. Iniciando entrenamiento desde cero.")
# Clean up old checkpoints if there are more than 5
cleanup_old_checkpoints(OUTPUT_DIR, max_checkpoints=SAVE_TOTAL_LIMIT)
# Load dataset (cached if present)
logger.info("Cargando dataset (puede tardar)...")
dataset = load_dataset(DATASET_ID, split=SPLIT)
# If already tokenized (has input_ids column), skip tokenization
tokenized_already = 'input_ids' in dataset.column_names
# Tokenizer
logger.info("Cargando tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
def tokenize_function(batch):
key = "code" if "code" in batch else ("content" if "content" in batch else list(batch.keys())[0])
toks = tokenizer(batch[key], truncation=True, padding="max_length", max_length=MAX_LENGTH)
toks["labels"] = toks["input_ids"].copy()
return toks
if not tokenized_already:
logger.info("Tokenizando dataset (esto puede tardar; usa batched=True)...")
dataset = dataset.map(
tokenize_function,
batched=True,
batch_size=MAP_BATCH_SIZE,
remove_columns=[c for c in dataset.column_names],
cache_file_name=os.path.join(TOKENIZED_CACHE_DIR, "tokenized_dataset.arrow"),
)
else:
logger.info("Dataset ya tokenizado — saltando tokenización.")
dataset.set_format(type="torch", columns=["input_ids", "attention_mask", "labels"])
# Model
logger.info("Cargando modelo...")
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
try:
model.gradient_checkpointing_enable()
except Exception:
logger.debug("Gradient checkpointing not available for this model")
# Data collator
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
# Compute steps
try:
total_examples = len(dataset)
steps_per_epoch = math.ceil(total_examples / (BATCH_SIZE * GRADIENT_ACCUMULATION))
max_steps = steps_per_epoch * EPOCHS
logger.info(f"Total examples: {total_examples}, steps/epoch: {steps_per_epoch}, max_steps: {max_steps}")
except Exception:
steps_per_epoch = None
max_steps = None
# Training args
training_args = TrainingArguments(
output_dir=OUTPUT_DIR,
overwrite_output_dir=False,
num_train_epochs=EPOCHS,
per_device_train_batch_size=BATCH_SIZE,
gradient_accumulation_steps=GRADIENT_ACCUMULATION,
fp16=(device == "cuda"),
save_strategy="steps",
save_steps=SAVE_STEPS,
save_total_limit=SAVE_TOTAL_LIMIT,
logging_steps=LOGGING_STEPS,
dataloader_num_workers=0, # Cambiado a 0 para evitar overhead en móvil
dataloader_pin_memory=False, # Desactivado para CPU
remove_unused_columns=False,
report_to=[],
resume_from_checkpoint=last_checkpoint, # Auto-resume from last checkpoint
# Optimizaciones adicionales para móvil
gradient_checkpointing=True,
optim="adamw_torch", # Optimizador más rápido
max_grad_norm=1.0,
)
# Callbacks
resource_cb = ResourceMonitorCallback(
check_every_n=CHECK_RESOURCES_EVERY_N_STEPS,
mem_threshold=MEMORY_THRESHOLD,
cpu_threshold=CPU_THRESHOLD,
)
progress_cb = TqdmProgressCallback(total_steps=max_steps)
cleanup_cb = CheckpointCleanupCallback(output_dir=OUTPUT_DIR, max_checkpoints=SAVE_TOTAL_LIMIT)
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=dataset,
callbacks=[resource_cb, progress_cb, cleanup_cb],
)
# Wrap training loop to support graceful save on SIGINT or resource request
if last_checkpoint:
logger.info(f"Inicio de entrenamiento — {EPOCHS} epochs configuradas (reanudando desde checkpoint).")
else:
logger.info(f"Inicio de entrenamiento — {EPOCHS} epochs configuradas.")
# Helper to do a manual save
def manual_save(tag: str = None):
dest = OUTPUT_DIR
if tag:
dest = os.path.join(OUTPUT_DIR, f"manual-{tag}")
logger.info(f"Guardando modelo manualmente en: {dest}")
trainer.save_model(dest)
tokenizer.save_pretrained(dest)
# Start training with periodic checks
try:
# We can't easily interrupt trainer.train() internally, so we rely on callbacks and SIGINT
# Pass resume_from_checkpoint to continue from where we left off
trainer.train(resume_from_checkpoint=last_checkpoint)
except KeyboardInterrupt:
logger.warning("Interrupción por el usuario detectada. Guardando checkpoint...")
try:
manual_save(tag=f"step{trainer.state.global_step}")
except Exception as e:
logger.exception("Error al guardar el checkpoint durante interrupción: %s", e)
logger.info("Guardado finalizado. Saliendo.")
sys.exit(0)
# Final save
logger.info("Entrenamiento terminado. Guardando modelo final...")
manual_save(tag="final")
logger.info("Entrenamiento completado. Modelo guardado en: %s", OUTPUT_DIR)
if __name__ == "__main__":
main()