hanzo-train 0.1.0

Hanzo Train — Tinker-shaped client for the Hanzo Engine training API

hanzo-train

Tinker-shaped Python client for the Hanzo Engine training API.

It mirrors the shapes of Thinking Machines' tinker SDK, so training loops written against tinker port across unchanged, while using Hanzo-canonical field names. The transport is synchronous httpx with no retries; the four training ops return a completed future exposing .result(timeout=None) so fut.result() code ports 1:1.

Install

pip install hanzo-train

Quickstart

from hanzo_train import ServiceClient, LoraConfig, AdamParams, SamplingParams, Datum, ModelInput

sc = ServiceClient(base_url="http://localhost:1234", api_key=None)   # api_key -> Authorization: Bearer

tc = sc.create_lora_training_client(
    "HuggingFaceTB/SmolLM2-135M", lora_config=LoraConfig(rank=16), wait=True, timeout=600.0
)
# wait=True polls the client until status is ready (raises on failed); wait=False returns immediately.

out = tc.forward_backward([
    {"prompt": "2+2=", "completion": "4"},
    Datum(model_input=ModelInput(tokens=[1, 2, 3]), target_tokens=[2, 3, 4], weights=[0.0, 1.0, 1.0]),
]).result()
# out.loss, out.num_tokens, out.metrics

tc.optim_step(AdamParams(lr=1e-4)).result()

resp = tc.sample(
    prompt="2+2=", sampling_params=SamplingParams(max_tokens=8, temperature=0.0), num_samples=1
).result()
# resp.sequences[0].tokens / .text

saved = tc.save_weights_and_get_sampling_client(name="my-adapter").result()   # .path, .format == "peft"
# alias: tc.save_weights(name="my-adapter")

info = tc.get_info()          # TrainingClientInfo incl. loss_history
sc.list_training_clients()    # list[TrainingClientInfo]
tc.delete()

Errors

Every non-2xx response raises HanzoTrainError(status, message):

• 400 — bad input
• 404 — unknown training client id
• 409 — client still loading, or failed to load

API

ServiceClient(base_url, api_key=None)

• create_lora_training_client(base_model, lora_config=None, wait=True, timeout=600.0, poll_interval=1.0) -> TrainingClient
• list_training_clients() -> list[TrainingClientInfo]
• close()

TrainingClient

• forward_backward(data) -> Future[ForwardBackwardResult] — data items are either {"prompt": str, "completion": str} dicts or Datum values.
• optim_step(adam_params=None) -> Future[OptimResult]
• sample(prompt=None, tokens=None, sampling_params=None, num_samples=1) -> Future[SampleResult] — pass exactly one of prompt or tokens.
• save_weights_and_get_sampling_client(name, dir=None) -> Future[SaveResult] (alias: save_weights)
• get_info() -> TrainingClientInfo
• delete()