Cookbook - posttrainllm with smolagents

Cookbook - posttrainllm with smolagents

What you get:

• A posttrainllm specialist served through the OpenAI-compatible API.
• A Hugging Face smolagents agent pointed at that local endpoint.
• One tool-calling demo you can run from a clean clone.
• A benchmark command to compare the specialist against a general model.
• A clear limit: this does not train the specialist for you.

What this is not: a claim that a tiny model beats a frontier model on broad agent tasks. This is for narrow specialists: function calls, schemas, repo idioms, and other repeated local patterns.

1. Train Or Pick A Specialist

For function calling, use the distillation flow in distillation-fc.md. The short version is:

posttrainllm distill \
  --teacher microsoft/Phi-3-mini-4k-instruct \
  --student ~/.cache/posttrainllm/runs/huge-base-v1/huge-base-v1.tinygpt \
  --task function-calling \
  --out /tmp/posttrainllm-fc-specialist.tinygpt

If you already have a checkpoint, set:

export TINYGPT_MODEL=/tmp/posttrainllm-fc-specialist.tinygpt

2. Serve The Model

posttrainllm serve "$TINYGPT_MODEL" --host 127.0.0.1 --port 8080

posttrainllm exposes /v1/chat/completions, /v1/completions, and /v1/models. smolagents can use either OpenAIServerModel or LiteLLMModel; this recipe uses OpenAIServerModel because it directly accepts api_base.

3. Run smolagents

Install:

python3 -m pip install smolagents openai

Minimal agent:

from smolagents import CodeAgent, OpenAIServerModel, tool

model = OpenAIServerModel(
    model_id="posttrainllm",
    api_base="http://127.0.0.1:8080/v1",
    api_key="not-needed",
)

@tool
def lookup_status(ticket_id: str) -> str:
    """Return the status for a support ticket."""
    return {"A-100": "ready", "B-200": "blocked"}.get(ticket_id, "unknown")

agent = CodeAgent(tools=[lookup_status], model=model)
print(agent.run("Check ticket A-100 and answer in one sentence."))

Runnable example:

examples/smolagents-posttrainllm/run.sh

4. Benchmark

Use the same prompt set for the specialist and the general baseline:

posttrainllm run-bench \
  --model "$TINYGPT_MODEL" \
  --tasks tool-routing \
  --limit 50 \
  --out docs/artifacts/smolagents-specialist-bench.jsonl

Record:

Honest Limitations

• smolagents will only be as good as the model’s tool-call formatting.
• Native posttrainllm serve is single-process and best for local demos/evals.
• Do not claim a win until the benchmark table is filled with real numbers.

See also: Pydantic AI and personal code specialist.