from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Ailiance-fr/qwen3-4b-mascarade-iot-lora", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

TRL: 1.4.0
Transformers: 5.8.0
Pytorch: 2.11.0
Datasets: 4.8.5
Tokenizers: 0.22.2

Bench results — iact-bench Phase 8 (Docker validators, 2026-05-11)

Functional eval via the iact-bench v0.2 Docker sandbox validators (no-network, read-only rootfs, uid 1000 dropped caps, --tmpfs /tmp:size=256m). Source LoRA: Ailiance-fr/qwen3-4b-mascarade-iot-lora. Eval samples drawn with seed=101 from Ailiance-fr/mascarade-iot-dataset.

Metric	Value
Validator	`judge-only`
Tmpfs cap	`256m`
Samples	10
Pass rate	0.0
Total validator wall-clock	0.0s

Pass = sandboxed compile/exec succeeds (exit_code=0). See iact-bench/configs/domain_validators.yaml for the exact toolchain invocation (arm-none-eabi-gcc / arduino-cli / freecadcmd depending on domain).

Upstream base model — official evaluations

These are the official scores for the unmodified base model Qwen/Qwen3-4B-Instruct-2507, reported by Alibaba Qwen team. They represent the floor of capability that this LoRA inherits before the hardware-domain fine-tune adapts behavior.

Category	Benchmark	Qwen3-4B-Instruct-2507
Knowledge	MMLU-Pro	69.6
Knowledge	MMLU-Redux	84.2
Knowledge	GPQA	62.0
Knowledge	SuperGPQA	42.8
Reasoning	AIME25	47.4
Reasoning	HMMT25	31.0
Reasoning	ZebraLogic	80.2
Reasoning	LiveBench 2024-11-25	63.0
Coding	LiveCodeBench v6	35.1
Coding	MultiPL-E	76.8
Coding	Aider-Polyglot	12.9
Alignment	IFEval	83.4
Alignment	Arena-Hard v2	43.4
Alignment	Creative Writing v3	83.5
Alignment	WritingBench	83.4
Agent	BFCL-v3	61.9
Agent	TAU1-Retail	48.7
Agent	TAU1-Airline	32.0
Agent	TAU2-Retail	40.4
Multilingual	MultiIF	69.0
Multilingual	MMLU-ProX	61.6
Multilingual	INCLUDE	60.1
Multilingual	PolyMATH	31.1

Source: official Qwen3-4B-Instruct-2507 model card.

Reading these numbers alongside the Phase 6 bench above: the upstream scores measure general capability (knowledge, reasoning, coding, alignment). The Phase 6 deltas measure hardware-domain specialization (KiCad, SPICE, schematic extraction). A rank-16 LoRA adapter modifies less than 1% of base weights, so the upstream scores remain approximately the floor — this LoRA adds the Phase 6 deltas on top of these inherited capabilities.

Citations

Cite TRL as:

bibtex

@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}

Bench comparison (2026-05-11)

Base model (Qwen3-4B-Instruct-2507) capability

No baseline bench yet for Qwen3-4B-Instruct-2507 in our pipeline.

This LoRA (tuned) — bench PENDING

Will include iact-bench Docker validators per domain + N3 5-axis kicad-cli. Production usage: compiled to Tower Ollama Modelfile, served via gateway alias ailiance-<domain> (kicad/spice/stm32/emc/embedded/platformio/freecad/dsp/iot/power).

Cross-domain forgetting check (Phase 9, 2026-05-11)

For each domain's eval set (seed=101, n samples held-out), compare this LoRA's Jaccard token-overlap vs the Qwen3-4B-Instruct-2507 baseline (no adapter) on the SAME prompts. Negative Δ = the LoRA degrades base behaviour on that domain.

Eval domain	LoRA Jaccard	Δ vs base
`kicad`	0.083	-0.004
`spice`	0.006	+0.001
`stm32`	0.075	+0.025
`emc`	0.069	+0.003
`embedded`	0.085	+0.011
`platformio`	0.057	+0.015
`freecad`	0.035	+0.014
`dsp`	0.109	+0.008
`iot`	0.06	-0.008 ⬅ in-domain
`power`	0.076	+0.008

In-domain Δ: -0.008 Out-of-domain mean Δ: 0.009