What Is NeoSmith?
NeoSmith is a cloud-native platform that automates knowledge distillation — the process of transferring capabilities from large foundation models (like GPT-4 or Claude) into small language models with fewer than 1 billion parameters. These distilled SLMs are purpose-built for agentic AI use cases: tool calling, multi-step reasoning, structured data extraction, and autonomous task execution.
Traditional model distillation requires deep ML expertise, weeks of experimentation, and expensive GPU infrastructure. NeoSmith reduces this to a single API call. Define your agent's task, point to your training data, and NeoSmith handles architecture selection, training, evaluation, and deployment — all on managed cloud infrastructure.
Key Features
Automated Distillation Pipelines
Define a task specification and NeoSmith automatically selects the optimal student architecture, generates synthetic training data from your teacher model, runs distillation, and validates the output against your accuracy benchmarks. No ML infrastructure management required.
Agentic Workflow Optimization
SLMs distilled by NeoSmith are specifically tuned for agent behaviors — tool calling with structured JSON output, multi-step reasoning chains, context window management, and graceful error recovery. Your agents get faster, cheaper, and more reliable.
Sub-1B Parameter Models
Deploy models small enough to run on edge devices, in serverless functions, or alongside your application code. NeoSmith targets the sweet spot between capability and efficiency — typically 100M to 800M parameter models that outperform generic models 10x their size on your specific task.
Production-Ready Deployment
Export distilled models as ONNX, GGUF, or serve them directly via NeoSmith's inference API. Built-in A/B testing lets you compare your SLM against the teacher model before cutting over. Monitor accuracy drift with integrated eval pipelines.