crineforge 0.2.1

Forge intelligent models from raw data.

CrineForge

Forge intelligent text-trained models from raw documents with enterprise-grade reliability.

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🚀 What is CrineForge?

CrineForge is a lightweight, offline-first LLM fine-tuning toolkit designed to take you from raw documents to a fine-tuned LoRA model in minutes. It automatically structures raw text data using a powerful local structurer and fine-tunes HuggingFace models seamlessly.

It is designed to be safe, modular, and GPU-aware, providing exceptional performance out of the box.

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🎯 Who is it for?

• ML Engineers & AI Developers needing rapid, reliable fine-tuning pipelines.
• Local Sandbox Users testing models securely on private data.
• Enterprise Operations wanting structured training without heavy, complex configuration frameworks.

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✨ Key Features

• Blazing Fast Structuring: Powered by Qwen/Qwen2.5-1.5B-Instruct out-of-the-box for minimal VRAM footprint and high-speed JSON generation.
• Pro Mode Structuring: Optional DeepSeek 7B fallback for rigorous, enterprise-scale formatting.
• LoRA Fine-Tuning Support: Native integration with trl and peft.
• Automatic 4-bit Fallback: Zero-configuration fallback quantization for low VRAM GPUs.
• Gated Model Support: First-class support for HF_TOKEN authenticated models (e.g., Llama-3).
• VRAM Logging: Detailed tracking of Allocated & Reserved memory metrics.

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⚙️ System Requirements & Limitations

VRAM Requirements (Estimated)

GPU VRAM	Mode Availability
8 GB	4-bit LoRA (Default fallback)
16 GB	FP16/BF16 LoRA
24+ GB	Pro Mode Structurer (DeepSeek 7B) + FP16 Training

Note: VRAM usage varies depending on context length and batch size.

Limitations

• The default max_seq_length is conservatively set to 512 to prevent OOM errors on standard hardware.
• Structurer models require an initial download which may take time depending on your network.

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⚡ Quickstart

pip install crineforge

import os
from crineforge import Trainer

# Optional: Enable authenticated access to gated models
# os.environ["HF_TOKEN"] = "your_huggingface_token"

trainer = Trainer()
trainer.connect_model("sshleifer/tiny-gpt2")
trainer.load_data("data.txt")
trainer.auto_config()
trainer.train()
trainer.save("output_model")

🧠 Pro Mode (Heavyweight Structurer)

For power users with abundant VRAM, you can enable the DeepSeek 7B structurer:

trainer = Trainer(structurer_model="deepseek-ai/deepseek-llm-7b-chat")

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📊 Performance & Optimization

• Efficient Structurer: The default lightweight structurer (Qwen 1.5B) is utilized for performance, avoiding the heavy VRAM constraints of larger models.
• Lazy-Loaded: The structurer is exclusively deployed at generation time.
• VRAM Clearance: The structurer unloads natively before fine-tuning begins to prevent VRAM spikes.
• Checkpointing: Explicit gradient_checkpointing automatically supported.

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🔥 Important Disclaimer

Note:
CrineForge does not redistribute model weights.
Models are downloaded from their official sources and are subject to their respective licenses.

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📄 License

Crineforge is licensed under the MIT License. Copyright (c) 2025 Abhishek.