pllmQwen3 and mini-coder
Dense Qwen3 lowering with explicit head width and Q/K normalization.
pllm.qwen3.v1 extends dense lowering with explicit head_dim, per-head Q/K RMS normalization, and configuration-controlled attention bias. Attention projection width is num_attention_heads * head_dim; it is not assumed equal to hidden width.
The checked fixture is ricdomolm/mini-coder-4b revision c87892d046a62415a349a6b3aa244bb630fb7417, raw configuration SHA-256 fdbc9e0615fcb88b2cc37aa2b23fa332b3863068d50092aa6bd1e628fd187c92. This establishes configuration parsing and semantic-plan validation only.
Python SDK example
from pathlib import Path
from pllm import lower_model
fixture = Path("crates/pllm-models/tests/fixtures/mini-coder-4b-c87892d-config.json")
plan = lower_model(fixture.read_bytes(), batch=1, max_input_tokens=16, max_new_tokens=4)
operations = {operation["id"]: operation for operation in plan.prefill["operations"]}
assert operations["layer.0.q_norm"]["operator"] == "rms_norm"
assert operations["layer.0.rope_q"]["output_shape"][-1] == 128API: Python SDK objects and signatures
Run this example from a PLLM source checkout; package installations do not include the pinned mini-coder fixture. This does not execute the model.