The 'Tolstoy' model is a Low-Rank Adaptation fine-tune of Deep Seq R1 on 475 public-domain Russian classics and critiques for literary analysis.

Nvidia’s NeMo (Nim) ecosystem supports small-model fine-tuning and inference across edge devices, aligning chip vendor goals with LLM deployment.

VibeThinker is a 1.5B-parameter dense LLM fine-tuned from Quen 2.5 math 1B for reasoning tasks at a training cost of $8k, rivaling 20B-parameter model...Show more

Google’s Alpha Earth is a vectorized global earth dataset that transforms raw geospatial information into a massive searchable vector database.

"Vibethinker is a 1.5 billion parameter dense language model with a total training cost of 8 grand and achieves reasoning performance comparable to la...Show more

Tom joked that two guys who have never fine-tuned a model spent half an hour criticizing fine-tuning practices.

Offer domain-specific LLMs fine-tuned on public-domain corpora (e.g., 475 Russian classics) for specialized content analysis and conversational agents...Show more

Pack small fine-tuned models into mobile or robot applications for local inference tasks like bar-talk or firefighting support, avoiding cloud-based i...Show more

Most problems attributed to fine-tuning may be solved simply by formatting and pointing a powerful pre-trained model at the right data, rather than co...Show more

Instead of fine-tuning a model on custom data, point a strong LLM at a vectorized global dataset (e.g., Google’s Alpha Earth) and format the input cor...Show more

Develop tooling to assemble, vectorize, and align multimodal datasets (images, 3D models, floor plans) to streamline fine-tuning of domain-specific AI...Show more

Offer a managed service that continuously fine-tunes small open-source models on clients’ local hardware to avoid recurring cloud costs and stale mode...Show more

Build a specialized model that reasons across satellite imagery, floor plans, and 3D schematics to generate actionable property insights for real esta...Show more

Fine-tuned models become static products that go out of date, whereas renting evolving base models lets you benefit from continuous upstream improveme...Show more

Fine-tuning large models is often unnecessary when prompt engineering, retrieval augmented generation, and memory architectures can achieve the same u...Show more

Leverage agent architectures combined with memory and structured context engineering to deliver user outcomes without relying on fine-tuning.

Use RAG and memory context processing (MCP) techniques to inject up-to-date context into prompts rather than fine-tuning static models.

Implement a continuous fine-tuning process on small, open-source models running locally to keep models up-to-date without incurring high cloud costs.

Claude Sonnet 4.5 represents a high-performance LLM that is prohibitively expensive for high-volume use cases without custom fine-tuning to reduce rel...Show more

Cursor’s Composer is a fine-tuned coding model optimized for speed and efficiency rather than cutting-edge performance, ideal for repetitive code comp...Show more