Free Your Specialized Knowledge
Instantly migrate fine-tuned LoRA adapters across any LLM architecture. Stop retraining. Start translating.
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The Problem
AI Lock-In is Costing You.
Every fine-tuned LoRA adapter is a masterpiece of specialized knowledge, locked into a single LLM's architecture. When a better model arrives, your expertise is trapped. Retraining from scratch is slow, expensive, and often impossible without the original data.
Wasted Compute
Countless GPU-hours are spent re-teaching models the same skills on new architectures.
Knowledge Decay
Valuable, domain-specific adaptations are abandoned with obsolete models.
Data Privacy Risks
Retraining often requires re-accessing sensitive data, creating unnecessary risk.
The Solution
A Universal Translator for AI Skills.
Botcraft.ai introduces Activation-Space Mapping (ASM) to create a universal translation layer for LoRA adapters. We don't retrain your adapter; we teach the new model how to understand it.
Llama-3-8B
Botcraft.ai
Mapper Framework
Mistral-7B
Bring adapters from local files or directly from Hugging Face. Convert from any source to any target LLM we support.
How It Works
Fidelity Through Dual Alignment
Our Mapper framework uses a novel dual alignment technique. We don't just guess at translations; we create a precise mapping between the activation spaces of the source and target models.
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Behavioral Alignment
We ensure the target model reacts to inputs in the same way the source model did with the LoRA.
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Geometric Alignment
We map the geometric space of the LoRA's weights, preserving the fine-tuned skill's core structure.
The result: 85-95% performance transfer with a tiny fraction of the compute cost and zero data exposure. It's portability without compromise.
from CAST import Mapper
# Load your local or HF LoRA
source_lora = "path/to/your/lora"
# Define source and target models
source_model = "meta-llama/Llama-3-8B"
target_model = "mistralai/Mistral-7B-v0.1"
# Initialize the mapper
mapper = Mapper(source_model, target_model)
# Translate!
translated_lora = mapper.translate(source_lora)
# Save your new, portable adapter
translated_lora.save("path/to/new/lora")
> Success! Adapter translated.
Get Early Access to Botcraft.ai
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