Nebius Turns Token Factory Into Runnable AI Recipes for Developers
Nebius published an open cookbook of notebooks and reference apps for its Token Factory inference service, covering agents, RAG, fine-tuning, and distillation.
- Nebius published an open cookbook of notebooks for its Token Factory inference platform.
- Covers agents in CrewAI, LangChain, Agno, Google ADK, Pydantic AI, LlamaIndex, and AWS Strands.
- RAG examples pair Qwen3 embeddings with Milvus, Qdrant, or Weaviate vector stores.
- Includes LoRA fine-tuning, full post-training, and a worked distillation example for grammar checking.
- Backed by an OpenAI-compatible API supporting DeepSeek, GPT-OSS, Llama, Nemotron, and Qwen.
- MIT-licensed and available on GitHub with 260+ commits.
Nebius turns Token Factory into runnable recipes
Nebius has expanded its Token Factory companion project into a broad collection of production-oriented examples. The MIT-licensed cookbook repository contains Jupyter notebooks and runnable applications covering API access, agents, retrieval-augmented generation, fine-tuning, distillation, structured output, and media generation.
Its OpenAI-compatible interface lets developers reuse familiar SDK request formats while directing traffic to hosted open-weight models. Switching providers can require only a new base URL, API key, and model name, although provider-specific features and response formats may still require code changes.
One repo, six layers
Most examples are notebooks designed for inspection and modification. Each directory addresses a distinct part of the LLM application stack:
| Area | What it covers |
|---|---|
| API clients | A native API notebook, plus integrations for LiteLLM, ai-suite, and LlamaIndex. |
| Agents | Examples using CrewAI, Agno, LangChain, Google ADK, LlamaIndex, Pydantic AI, and AWS Strands. |
| RAG | PDF chat, Qdrant with Qwen3 embeddings, Milvus with GPT-OSS, and a LangGraph support agent backed by Weaviate. |
| Model adaptation | LoRA adapters, full-model fine-tuning, and a grammar-model distillation walkthrough. LoRA updates a small set of adapter weights, reducing the compute and storage required for customization. |
| Inference controls | Tool calling, batch inference, and guided JSON generation for outputs that must follow a defined schema. |
This story is for Pro members
You've reached the end of the free preview. Upgrade to AlphaSignal Pro to read the full article - and everything else behind the paywall.
Comments
