LLM Fine-Tuning Services

Overview

LLM fine-tuning services “domesticate” general-purpose open-source large models into dedicated models that understand your specific domain. Simo Intelligence provides the full pipeline — base model selection, data engineering, fine-tuning, evaluation, and private deployment — so institutions without an in-house algorithm team can own a domain-specific large model.

Core Features

1. Base Model Selection

• Choose from mainstream open-source bases such as ChatGLM, Qwen, LLaMA, and DeepSeek based on business scenarios, data scale, and compute budget
• Evaluate 7B / 13B / 70B options to balance performance and inference cost
• Support domain-specific selection for medical, research, finance, and government sectors

2. Data Engineering

• Corpus cleaning, deduplication, de-identification, and format standardization
• Instruction data construction: generate high-quality Q&A pairs and task instructions for business scenarios
• Data quality evaluation to filter low-quality samples and raise the fine-tuning ceiling

3. Efficient Fine-Tuning

• Full-parameter, LoRA, and QLoRA fine-tuning methods, selected as needed
• Supervised fine-tuning (SFT) as the foundation, with optional DPO / RLHF alignment
• Full training monitoring with loss curves and intermediate checkpoints

4. Evaluation & Delivery

• Build business evaluation sets and score across accuracy, stability, and safety
• Deliver model weights, inference services, APIs, and fine-tuning scripts
• Support local server / private-cloud deployment, keeping data within your domain

Use Cases

• Healthcare: medical Q&A, assisted diagnosis, medical-record structuring, literature interpretation
• Research Institutions: domain knowledge assistants, data analysis, publication support
• Enterprise Services: internal knowledge-base Q&A, intelligent customer service, process automation

Technical Approach

For the full product introduction and case studies, visit our dedicated platform at www.simolm.com.