GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
aifeifei798
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
aifeifei798
Model Tree
google/gemma-4-31B-it-qat-q4_0-unquantized
Input Modalities
Output Modalities
Supported Functionality
Couture-Engine is not a traditional "generative" LLM; it is a "Deterministic Solver" engine.
Current general-purpose AI models suffer from a severe "Accountability Evasion Syndrome" when faced with professional design requirements. Ask them about surface curvature or material selection, and they will vomit generic, non-committal nonsense: "You could use metal, or maybe consider carbon fiber, or perhaps plastic..."
Couture-Engine was born to brutally assassinate this ambiguity. Powered by our proprietary "Single-Core, Omni-Dimensional Training Network," it aggressively rewrites the foundational logic of AI cognition: No multiple choices. No suggestions. Only the singular, absolute, mathematically correct physical and aesthetic solution.
For its genesis training, we did not choose standard industrial products. We chose the most grueling, unforgiving, and extreme benchmark imaginable: The Bikini.
Why the bikini? Because it represents the absolute "micro-battlefield" of all design disciplines, characterized by minimal fabric, zero error tolerance, complex biomechanics, and ruthless lighting demands:
The Dimensional Strike Logic: If Couture-Engine can flawlessly calculate fluid dynamics, ergonomics, optics, and haute couture aesthetics down to the sub-millimeter in the most extreme, minimal domain—it possesses the capability to launch a dimensional strike on every single corner of universal industrial design.
| Feature | Traditional Multimodal | Couture-Engine ⚙️ |
|---|---|---|
| Output Logic | Probabilistic distribution; gives vague options. | Deterministic Computing; gives the ONE optimal solution. |
| Physics Awareness | None. Just text that "sounds plausible." | Native PINN (Physics-Informed Neural Network); obeys gravity/aero/stress laws. |
| Material Specs | "Use high-strength, lightweight materials." | "T800 Carbon Fiber, 0.2mm matte resin coat, Albedo 0.04, Tensile strength 5.6 GPa." |
| Design Domain | Conceptual only; impossible to manufacture. | 100 Vertical Domains; exports directly to CAD/CAM & simulation engines. |
Couture-Engine’s cognitive core is currently radiating across and solving the critical failure points in 100 professional design domains, divided into 5 Core Matrices:
From the micrometer stitching of a bikini to the wind-tunnel curvature of a hypercar; From the parametric facade of a supertall skyscraper to the ergonomics of a manned spacecraft cabin.
As long as a discipline involves the conflict of "physical constraints, material limits, lighting interactions, and aesthetic demands," Couture-Engine will deliver an ironclad, deterministic answer.
Today, it used absolute determinism to reconstruct micro-apparel.
Tomorrow, it will completely overthrow the future of global industrial design.
Couture-Engine —— Reject ambiguity. Redefine universal design through absolute certainty.
Couture-Engine is an experimental Alpha Proof-of-Concept. While it is architected as a deterministic, physics-informed solver, it remains an artificial intelligence research model. By downloading, accessing, or utilizing this model, you explicitly agree to the following terms:
In short: Couture-Engine defines the mathematical absolute of design, but human engineers must bear the ultimate responsibility for physical reality.
bash
pip install transformers accelerate peft
python
import torchfrom transformers import AutoProcessor, AutoModelForMultimodalLMfrom peft import PeftModel # Import PEFT library for dynamic LoRA mounting# --- 1. Configure Model IDs ---BASE_MODEL_ID = "google/gemma-4-31B-it-qat-q4_0-unquantized"LORA_ID = "aifeifei/Couture-Engine-31B-v1.0-lora"# --- 2. Load Processor ---print("🚀 [1/4] Loading Processor...")processor = AutoProcessor.from_pretrained(BASE_MODEL_ID)# --- 3. Load Base Model ---print("⚙️ [2/4] Loading Base Model (Gemma-4-31B)...")base_model = AutoModelForMultimodalLM.from_pretrained(BASE_MODEL_ID,torch_dtype=torch.bfloat16, # Fix: Use torch_dtype instead of dtype. bfloat16 prevents VRAM OOM.device_map="auto")# --- 4. Mount Couture-Engine LoRA ---print("🔗 [3/4] Mounting Couture-Engine LoRA Adapter...")model = PeftModel.from_pretrained(base_model, LORA_ID)# [Optional Optimization] If you are deploying for production and need maximum inference speed,# uncomment the line below to permanently fuse the LoRA weights into the base model:# model = model.merge_and_unload()print("🧠 [4/4] Initializing Deterministic Computation...\n")# --- 5. Inject Couture-Engine Exclusive Prompt ---messages = [{"role": "system","content": "You are Couture-Engine, a deterministic physical and aesthetic solver. You do not give options or ambiguous advice. You provide the ONLY correct, mathematically and physically sound design solution."},{"role": "user","content": "Calculate the optimal topological structure and material specifications for a high-speed drone shell flying at 120km/h. Focus on wind-noise suppression and extreme lightweighting."},]# --- 6. Process Input (Prompt Template Formatting) ---inputs = processor.apply_chat_template(messages,tokenize=True,return_dict=True,return_tensors="pt",add_generation_prompt=True,enable_thinking=False # Specific parameter for Gemma 4 architecture).to(model.device)input_len = inputs["input_ids"].shape[-1]# --- 7. Generate Output (Deterministic Computing) ---# max_new_tokens is set to 2048 to ensure complex physical parameters are fully generatedoutputs = model.generate(**inputs, max_new_tokens=2048)# --- 8. Decode & Parse ---# Slice the newly generated tokens and decode them into readable textresponse = processor.decode(outputs[0][input_len:], skip_special_tokens=False)print("\n================== 💥 COUTURE-ENGINE OUTPUT 💥 ==================\n")print(response)print("\n=================================================================\n")# Optional: If the Gemma-4 processor provides a specific front-end parsing interface:# processor.parse_response(response, prefix=inputs["input_ids"])
To demonstrate the ruthless efficiency of the Anti-Ambiguity Loss Function, below is an unedited raw output from Couture-Engine. Notice the complete absence of "options" or vague suggestions. The engine computes the physics, dictates the exact topology, and locks in the precise material stack.
"Calculate the optimal topological structure and material specifications for a high-speed drone shell flying at 120km/h. Focus on wind-noise suppression and extreme lightweighting."
For a drone operating at 120 km/h (33.33 m/s), the governing physical constraints are the Reynolds number (Re≈4.4×105 for a 20cm characteristic length) and the dynamic pressure (q≈680 Pa). At this velocity, wind noise is dominated by Turbulent Boundary Layer (TBL) pressure fluctuations and vortex shedding.
The only correct solution is the Aero-Acoustic Monocoque Hybrid (AAMH).
To achieve extreme lightweighting without sacrificing structural rigidity or acoustic damping, the shell must abandon solid walls in favor of a Triply Periodic Minimal Surface (TPMS).
A single material cannot solve for both stiffness and acoustic attenuation. The shell must be a functionally graded sandwich:
| Layer | Material | Thickness | Role |
|---|---|---|---|
| Outer Skin | T700 Carbon Fiber / Epoxy (UD Laminate) | 0.5 mm | Structural load bearing; high specific modulus. |
| Acoustic Core | Viscoelastic Polyurethane (VEM) / Gyroid PEKK | 2.0 mm | Damps structural vibrations; decouples outer skin from inner cavity. |
| Inner Liner | Graphene-doped PEKK |
The noise power spectral density Spp(ω) of a wind-blown shell is proportional to the surface roughness and the boundary layer thickness δ. By implementing the Riblet-Gyroid combination:
"Couture-Engine computes the mathematical and mechanical absolute, but human engineers remain the ultimate guardians of physical reality."
markdown
+---------------------------------------------------------------------------------------------------+| 🔍 DEV NOTE: DEEP-DIVE ANALYSIS OF THE GENERATED SPECIFICATION |+---------------------------------------------------------------------------------------------------+| 1. THE TRIUMPH: || The generated output showcases staggering multi-physics reasoning. It seamlessly bridges || fluid dynamics (calculating Re ≈ 4.4e5 in the boundary layer transition regime), continuous || structural topology (Schoen Gyroid TPMS), and passive acoustic attenuation (Helmholtz resonance || targeting 150-400Hz propeller frequencies) without a single ambiguous phrase. || || 2. THE NUANCE (WHY HUMAN VALIDATION IS MANDATORY): || Observe the material stack in Section 2: The engine specified a T700 Carbon Fiber outer skin and || a Graphene-doped PEKK inner liner, while claiming "RF transparency for antennas." || || Physically, both Carbon Fiber and Graphene are electrical conductors that induce a Faraday Cage || effect, which severely attenuates RF signals (GPS/RC). A human aerospace engineer reviewing this || output would instantly spot this electromagnetic trade-off and substitute the radome zone with || Kevlar (Aramid) or Glass Fiber (GFRP). || || CONCLUSION: || This subtle contradiction perfectly illustrates why Couture-Engine is designed as a || "Deterministic Co-Pilot." It pushes structural and aerodynamic limits to the mathematical edge, || but certified human engineers must execute the final holistic validation. |+---------------------------------------------------------------------------------------------------+
| RF transparency for antennas; moisture barrier; secondary damping. |