Lesson 630 of 2116
Function Calling With Local Models: Harness First, Model Second
Function calling with local models works only when the harness validates schemas, rejects malformed calls, and controls tools.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The operational idea: local function calling
- 2function calling
- 3tool schema
- 4harness
Concept cluster
Terms to connect while reading
Section 1
The operational idea: local function calling
Function calling with local models works only when the harness validates schemas, rejects malformed calls, and controls tools. In local AI, the model family is only one part of the system. The runtime, file format, serving path, hardware budget, evaluation set, and safety policy decide whether the model becomes useful.
Compare the options
| Layer | What to decide | What can go wrong |
|---|---|---|
| Runtime | local function calling | The model runs, but the workflow is slow or brittle |
| Evaluation | A small task-specific test set | A flashy demo hides routine failures |
| Safety and ops | Permissions, provenance, logging, and rollback | Executing tool calls because the text looks plausible instead of parsing, validating, and allowlisting them. |
Current source signal
Build the small version
Implement one fake tool and force the local model to call it through a validated JSON schema.
- 1Define the user task in one sentence.
- 2Choose the smallest model and runtime that might pass that task.
- 3Run one happy-path prompt and one failure-path prompt.
- 4Record speed, memory pressure, output quality, and the exact reason for any failure.
- 5Write the operating rule you would give a non-expert user.
A local-model operations sketch students can adapt.
tool_call_harness:
model_output -> parse_json
if invalid: reprompt_with_error
if tool_not_allowed: refuse
if schema_invalid: reject
if high_risk: require_human
else: execute_tool_and_return_resultKey terms in this lesson
The big idea: harness first. A local model app is not done when the model answers once; it is done when the whole workflow can be installed, measured, trusted, and recovered.
End-of-lesson quiz
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