Flui3d Chat Model Qwen 3 Reasoning
Model Description
This model is a Fine-tuned version of Qwen 3 designed for microfluidic chip design generation . The model incorporates Chain-of-Thought (CoT) reasoning to translate high-level design requirements into structured microfluidic system descriptions.
The model generates outputs in a
structured JSON format following a predefined schema (see:
Output Format ). The generated JSON describes a
complete microfluidic chip , including:
microfluidic components
component parameters
channel connections
structural relationships between elements
This allows the model to act as a design file generator for microfluidic systems , enabling automated or AI-assisted microfluidic chip design workflows.
The repository includes:
LoRA Adapter weights Quantized, split GGUF model files compatible with Ollama, may require merging before use
GGUF files can be merged using tools provided by llama.cpp (see:
Merging Split GGUF Files ).
Intended Use
This model is intended for:
Automated microfluidic chip design generation AI-assisted CAD workflows for microfluidics Research in AI-assisted scientific design Programmatic generation of microfluidic device specifications
The model converts natural language design requirements into structured microfluidic design specifications .
Example Applications
Rapid prototyping of microfluidic devices
Automated generation of chip layouts
Integration with microfluidic CAD pipelines
AI-driven design exploration
Model Architecture
Base Model: Qwen 3 32BFine-tuning Method: Cold-start SFT LoRAReasoning Strategy: Chain-of-Thought prompting and supervisionOutput Format: Structured JSON
The model is trained to produce schema-compliant structured outputs representing microfluidic chip configurations.
Output Format
The model generates JSON objects conforming to a predefined schema.
Schema definition:
https://github.com/TUM-EDA/Flui3d-Chat/blob/master/Dataset%20and%20Training%20Framework/datasets/resources/json_schemas/microfluidic_schema.jsonThe JSON output typically includes:
Component definitions
Channel connections
Parameterized microfluidic elements
Junction definitions
Example Output
1 {
2 "connections" : [
3 {
4 "source" : "inlet_1" ,
5 "target" : "mixer_1"
6 } ,
7 {
8 "source" : "inlet_2" ,
9 "target" : "mixer_1"
10 } ,
11 {
12 "source" : "mixer_1" ,
13 "target" : "outlet_1"
14 }
15 ] ,
16 "junctions" : [
17 {
18 "id" : "junction_1" ,
19 "type" : "T-junction" ,
20 "source_1" : "inlet_1" ,
21 "source_2" : "inlet_2" ,
22 "target" : "mixer_1"
23 }
24 ] ,
25 "component_params" : {
26 "mixers" : [
27 {
28 "id" : "mixer_1" ,
29 "num_turnings" : 4
30 }
31 ] ,
32 "delays" : [ ] ,
33 "chambers" : [ ] ,
34 "filters" : [ ]
35 }
36
Repository Contents
This repository includes:
1. LoRA Adapter
The LoRA adapter can be loaded on top of the base Qwen model for inference or further fine-tuning.
2. Quantized GGUF Models
Quantized GGUF format models compatible with:
Due to file size limitations, the GGUF models are split into multiple parts . These files must be merged before use.
Merging Split GGUF Files
To merge the split GGUF files, use the merging utilities from llama.cpp :
https://github.com/ggml-org/llama.cpp/blob/master/tools/gguf-split/README.mdUsage with Ollama
The merged GGUF file can be used with:
Example prompt:
Design a microfluidic chip with two inlets, one mixer, and a single outlet.
Limitations
The model assumes valid schema-based output format and may produce invalid JSON if prompts are poorly structured.
Generated designs should be validated before fabrication .
The model does not replace domain expert verification .
Citation
If you use this model in academic work, please cite:
