From a capable teacher to an evaluated student.
BodhiVedam’s distillation workflow: task definition, learning data, teacher/student training, evaluation, model registry and deployment artifacts.
Read the full guideBodhiVedamModel distillation
Big-model knowledge. A smaller footprint.
A knowledge-distillation platform for turning teacher-model capabilities into smaller models you can evaluate, package and deploy.
For AI, platform and applied-ML teamsTalk technology
Knowledge, thoughtfully distilled.
Model distillation / DataVedam
The right model fits the work.
The largest model is not always the right deployment choice. Bodhi brings the distillation lifecycle together—from preparing task data and learning from a teacher to evaluating a student and selecting a deployment format for its intended environment.
Get to know
BodhiVedam.
Go inside the work: the inputs, decisions and implementation details that turn a product description into an informed evaluation.
Built for the
work around it.
For AI, platform and applied-ML teams.
Prepare the learning material
Organise the task, data and teacher-generated examples that will guide a smaller model. Treat data quality and permitted use as part of the model-development workflow.
Train the student
Work through teacher-to-student distillation and training workflows. Select model and method support for the task rather than assuming any teacher can produce any student.
Evaluate the trade-offs
Compare task quality and deployment constraints before choosing a model. Evaluate on representative work; smaller size alone is not evidence of a better result.
Package for deployment
Use model registry, serving and export surfaces to carry the chosen artifact toward its target runtime. Confirm compatibility with the hardware and inference stack.
A purposeful path
from idea to action.
A framework for the implementation conversation.
- 01
Choose the task
Define what the smaller model must do and the constraints it must meet.
- 02
Distil the capability
Prepare learning data and run the agreed teacher/student workflow.
- 03
Measure the result
Evaluate quality against a representative task set.
- 04
Fit the environment
Select the artifact and serving approach for the target deployment.
Inside an implementation.
Concrete workflows to explore with your team. The scope and connections are agreed around your environment.
Evaluate a smaller model for a defined task
An applied-AI team has a useful teacher-model workflow but wants to understand whether a smaller student can meet the task’s quality and deployment needs. The evaluation begins with the task, not an assumed saving.
- Define representative inputs and quality criteria
- Prepare permitted data and teacher-generated examples
- Run the selected teacher-to-student workflow
- Compare the student against the agreed task evaluation
What you’re working towardAn evaluated student artifact and a clearer account of the quality and deployment trade-offs.
Fit a model to a constrained environment
A platform team is considering a private, edge or hardware-constrained deployment. Model size, supported formats, inference behaviour and task quality all need to fit the same environment.
- Document hardware and runtime constraints
- Choose a compatible model and distillation approach
- Evaluate representative work under those constraints
- Package and validate the artifact in the target stack
What you’re working towardA deployment candidate whose compatibility and task behaviour have been assessed for the intended environment.
People stay in the pictureUseful intelligence.
Useful intelligence.
Considered control.
- Verify rights and permitted use for data and teacher outputs.
- Evaluate on your actual task, not a headline benchmark alone.
- Validate runtime and hardware compatibility before release.
Make it fit
your world.
A useful walkthrough starts with the people, information and systems involved in your work.
Task and learning material
Bring the task definition, representative examples and quality criteria. Establish permitted use for source data and teacher outputs before training.
Model and runtime support
Confirm supported teacher/student combinations, method, artifact format and serving requirements. Target hardware and inference-stack compatibility are part of the scope.
Measured trade-offs
Evaluate quality alongside training effort and serving constraints. Compare complete deployment economics; a smaller model is not by itself proof of lower cost or better performance.
A few good
questions.
Something more specific?
Talk to our team.
Is distillation the same as compressing a file?
No. It trains a smaller student to learn capabilities from a teacher. The result must be evaluated: reducing model size can change task quality and behaviour.
Does a smaller model always cost less?
No universal saving is promised. Training, serving, hardware, traffic and required quality all affect the economics. The evaluation should consider the complete deployment.
Can the student run at the edge?
Edge deployment is a potential target, subject to the chosen model, artifact format, hardware and inference runtime. Compatibility and task performance must be validated for that environment.
Where should we start?
Bring a bounded task, representative data and the deployment constraints. The first step is to determine whether distillation is appropriate and agree how success will be measured.
Start with the right scope.
Discuss a task-specific distillation evaluation. Model support, data requirements and deployment formats are confirmed during scoping.
Your next chapter
What could we
make possible?
Bring us the work that matters.
We’ll find the right place to begin.