Trained to accurately call 9 tools — lineage traversal, impact analysis, test coverage reporting, schema introspection, search, and more — with valid arguments and well-synthesized answers grounded in real tool output.

Model details


Base model	Qwen3.5-35B-A3B-Base
Architecture	Mixture of Experts (35B total, 3B active per token)
Fine-tuning method	bf16 LoRA (r=16, alpha=16)
Training stages	Stage 1: tool mechanics (1 epoch, 1,206 examples) / Stage 2: structured planning (2 epochs, 290 examples)
Hardware	NVIDIA H200 141GB, ~1 hour total
Training data	1,206 ChatML examples with real tool responses from indexed project metadata

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "mach-kernel/ecu-pilot-fp16",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("mach-kernel/ecu-pilot-fp16")

Quantized variants

Format	Repository
FP16 (this repo)	mach-kernel/ecu-pilot-fp16
LoRA adapter only	mach-kernel/ecu-pilot-fp16-lora
GGUF Q4_K_M	mach-kernel/ecu-pilot-q4km
GGUF Q8_0	mach-kernel/ecu-pilot-q8_0

Training methodology

Two-stage supervised fine-tuning adapted from the Thinkquel methodology:

Stage 1 — Tool mechanics: Teaches the model what tools exist, how to format calls, and how to interpret responses.
Stage 2 — Structured planning: Teaches the model to reason about when and why to call tools using <think> blocks before acting.

All training examples use real tool responses from an indexed project — no synthetic or hallucinated tool output.

Why "ecu"

No particular reason. Just liked the sound of it.

Why ecu

No reason. Just liked how it sounded. Definitely not a Caesar cipher of anything. Don't look into it.