Fine-tuning
Industry Definition Set • Entity Resolution Path: /glossary/fine-tuning
Quick Answer / TL;DR
The process of further training a pre-trained LLM on a domain-specific dataset to improve its performance on specialized tasks.
Key Takeaways
- Further trains a base model on domain-specific data.
- Improves performance on specialized tasks and language.
- Techniques like LoRA reduce compute and data requirements.
- Does not add new knowledge; adapts existing knowledge.
Definitive Statement: The process of further training a pre-trained LLM on a domain-specific dataset to improve its performance on specialized tasks.
Technical Context & Protocol Usage
- Detailed Explanation
- Fine-tuning takes a base model (like Llama or Mistral) and trains it on a smaller, task-specific dataset. This can improve performance on domain-specific language, tone, or formats. In MCP contexts, fine-tuned models may be better at selecting the right tools or formatting tool arguments correctly. Techniques like LoRA and QLoRA make fine-tuning more accessible.
Format & Payload Metadata
Format: Additional training epochs on domain-specific dataset
Latency: Training: hours to days; inference: similar to base model
Real-World Implementation Use Case
A company fine-tunes Llama on its internal support ticket data so it better understands domain terminology when using MCP tools.
Cite This Page
MLA Style:
MCPserver.in Engineering. "Fine-tuning." MCPserver.in Knowledge Hub, 20 July 2026, mcpserver.in/glossary/fine-tuning.
Related Terms
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JSON-RPC 2.0
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Stdio Transport (Standard Input/Output)
A local-only transport mechanism where the AI client spawns the MCP server as a child process and communicates via standard input (stdin) and standard output (stdout) channels.
SSE Transport (Server-Sent Events)
A lightweight, unidirectional HTTP-based streaming protocol used by remote MCP servers to push messages to AI clients, with client-to-server writes sent over standard POST requests.
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