SFT Training Test: Ministral (Vision)¶

# Environment Setup
import os
from dotenv import load_dotenv
load_dotenv()

# Force text-based progress instead of HTML widgets
os.environ["TQDM_NOTEBOOK"] = "false"

# CRITICAL: Import unsloth FIRST for proper TRL patching
import unsloth
from unsloth import FastVisionModel, is_bf16_supported
from unsloth.trainer import UnslothVisionDataCollator

import torch

# Environment summary
gpu = torch.cuda.get_device_name(0) if torch.cuda.is_available() else "CPU"
print(f"Environment: unsloth {unsloth.__version__}, PyTorch {torch.__version__}, {gpu}")
print(f"HF_TOKEN loaded: {'Yes' if os.environ.get('HF_TOKEN') else 'No'}")

# Load Ministral-3B with FastVisionModel for vision capabilities
MODEL_NAME = "unsloth/Ministral-3-3B-Reasoning-2512"
print(f"\nLoading {MODEL_NAME.split('/')[-1]} with FastVisionModel...")

model, tokenizer = FastVisionModel.from_pretrained(
    MODEL_NAME,
    load_in_4bit=True,
    use_gradient_checkpointing="unsloth",
)
print(f"Model loaded: {type(model).__name__}")

# Apply LoRA adapters for vision training
model = FastVisionModel.get_peft_model(
    model,
    finetune_vision_layers=True,
    finetune_language_layers=True,
    finetune_attention_modules=True,
    finetune_mlp_modules=True,
    r=16,
    lora_alpha=16,
    lora_dropout=0,
    bias="none",
    random_state=42,
)

trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
total = sum(p.numel() for p in model.parameters())
print(f"LoRA applied: {trainable:,} trainable / {total:,} total ({100*trainable/total:.2f}%)")

# Load vision dataset (LaTeX_OCR - 5 samples for testing)
from datasets import load_dataset
from trl import SFTTrainer, SFTConfig

dataset = load_dataset("unsloth/LaTeX_OCR", split="train[:5]")
instruction = "Write the LaTeX representation for this image."

def convert_to_conversation(sample):
    return {
        "messages": [
            {"role": "user", "content": [
                {"type": "text", "text": instruction},
                {"type": "image", "image": sample["image"]}
            ]},
            {"role": "assistant", "content": [
                {"type": "text", "text": sample["text"]}
            ]}
        ]
    }

converted_dataset = [convert_to_conversation(s) for s in dataset]
print(f"Dataset loaded: {len(converted_dataset)} vision samples")

# Post-training inference test with vision
FastVisionModel.for_inference(model)

test_image = dataset[0]["image"]
messages = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": instruction}]}]
input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
inputs = tokenizer(test_image, input_text, add_special_tokens=False, return_tensors="pt").to("cuda")

with torch.no_grad():
    output = model.generate(**inputs, max_new_tokens=64, temperature=1.5, min_p=0.1)

response = tokenizer.decode(output[0], skip_special_tokens=True)
# Clean up BPE artifacts from Ministral tokenizer (Ġ=space, Ċ=newline)
response = response.replace('Ġ', ' ').replace('Ċ', '\n').strip()

# Clear success/failure banner
print("=" * 60)
if SFT_VISION_SUPPORTED:
    print("SFT Training: SUPPORTED for Ministral (Vision)")
    print("Model: FastVisionModel + Ministral-3-3B-Reasoning-2512")
    print("Components: UnslothVisionDataCollator, LaTeX_OCR dataset")
else:
    print("SFT Training: NOT SUPPORTED for Ministral (Vision)")
    print("Reason: See error above")
print("=" * 60)
print(f"Sample generation:\n{response[-200:]}")

# Shutdown kernel to release all GPU memory
import IPython
print("Shutting down kernel to release GPU memory...")
app = IPython.Application.instance()
app.kernel.do_shutdown(restart=False)