dustylm

Run DustyLM, the 8M parameter robot vacuum language model, in a few lines.

pip install dustylm

from dustylm import DustyLM
model = DustyLM.from_pretrained("mkhordoo/dusty-8m-sft")
response = model.chat([{"role": "user", "content": "who are you?"}])
print(response["choices"][0]["message"]["content"])

# beep. i am a little robot. i clean floors and find crumbs.

Backends

These sizes describe the model files, not the complete Python environment. Installation size varies by platform. Both backends currently include PyTorch as an SDK dependency.

# ONNX backend
model = DustyLM.from_pretrained("mkhordoo/dusty-8m-sft", backend="onnx")

Loading Local Checkpoints

Pass a Hugging Face repository ID to download its model and tokenizer automatically, as shown in the quick-start example:

model = DustyLM.from_pretrained("mkhordoo/dusty-8m-sft")

For files already downloaded to your machine, pass their local directory instead. With the default filenames, that directory must contain tokenizer.json and either model.pt (PyTorch) or model_int8.onnx (ONNX):

model = DustyLM.from_pretrained("./my-checkpoint/")

You can use any local directory as the artifact root. model_file and tokenizer_file are resolved relative to that directory, so they may use different filenames or relative paths.

If you trained a model with the main dusty-lm repository, its checkpoint and tokenizer are stored in separate artifact directories. Load the promoted SFT checkpoint with:

from dustylm import DustyLM

model = DustyLM.from_pretrained(
    "artifacts/checkpoints",
    model_file="dusty8m_sft.pt",
    tokenizer_file="../tokenizers/dusty_tokenizer.json",
)

response = model.chat([{"role": "user", "content": "who are you?"}])

To inspect an intermediate checkpoint instead, change model_file to an existing step file such as dusty8m_sft_step_100.pt.

The SDK infers several architecture dimensions from PyTorch checkpoint shapes and currently expects DustyLM-compatible configurations.

API

DustyLM.from_pretrained

DustyLM.from_pretrained(
    repo_id_or_path: str = "mkhordoo/dusty-8m-sft",
    *,
    model_file: str | None = None,
    tokenizer_file: str | None = None,
    backend: str = "torch",
)

DustyLM.chat

model.chat(
    messages: list[dict],
    temperature: float = 0.7,
    max_tokens: int = 64,
    top_p: float = 0.9,
) -> dict

Returns an OpenAI-style chat completion dict.

Built from the dusty-lm training repository.