LLM Customization & Deployment
Connect domain tasks with data development, continued pretraining, post-training, model adaptation, and deployment. Let evaluation guide technical choices.
Updated October 10, 2026
Start with tasks and a baseline
Define tasks, input and output formats, data boundaries, and acceptance criteria. Establish a baseline with the current model before deciding whether adaptation adds value.
Choose a model adaptation path
Domain knowledge, output style, and task execution require different approaches. Select continued pretraining, instruction tuning, or post-training against data quality, evaluation feedback, and compute constraints. Retraining is not necessary for every application.
Deployment and regression evaluation
Consider task performance, inference cost, and response latency together. After integrating a model into an application or agent workflow, use operational feedback and regression tasks to track behavior changes.
Recent model candidates
Model choice is driven by your task rather than the size of our research checkpoints. We can assess customization and deployment paths for recent open-weight models, including Qwen3.8-Flash-Next and DeepSeek-V4.1-Flash。
Each project first checks license terms, available weights, inference tooling, compute requirements, and adaptation feasibility. Mistral Large 4 is currently in API public preview; its announced weights are expected at the end of October and are not treated here as already available. Status checked October 10, 2026.
Common questions
Does model customization always require pretraining?
No. Identify gaps in the existing model first, then choose an adaptation method based on data, compute, and evaluation results.
What should I prepare for an initial discussion?
Describe the task, a few examples, the current model or system, deployment constraints, and quality, cost, and latency targets. Do not send sensitive data through public channels.