---
license: llama3.3
base_model:
- meta-llama/Llama-3.3-70B-Instruct
---
# Flui3d Chat Model Llama 3.3 Reasoning

## Model Description

This model is a **Fine-tuned version of Llama 3.3** 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](#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](#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:** Llama 3.3 70B Instruct
* **Fine-tuning Method:** Cold-start SFT LoRA
* **Reasoning Strategy:** Chain-of-Thought prompting and supervision
* **Output 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.json
```

The JSON output typically includes:

* Component definitions
* Channel connections
* Parameterized microfluidic elements
* Junction definitions

### Example Output

```json
{
  "connections": [
    {
      "source": "inlet_1",
      "target": "mixer_1"
    },
    {
      "source": "inlet_2",
      "target": "mixer_1"
    },
    {
      "source": "mixer_1",
      "target": "outlet_1"
    }
  ],
  "junctions": [
    {
      "id": "junction_1",
      "type": "T-junction",
      "source_1": "inlet_1",
      "source_2": "inlet_2",
      "target": "mixer_1"
    }
  ],
  "component_params": {
    "mixers": [
      {
        "id": "mixer_1",
        "num_turnings": 4
      }
    ],
    "delays": [],
    "chambers": [],
    "filters": []
  }

```

---

# Repository Contents

This repository includes:

### 1. LoRA Adapter

The LoRA adapter can be loaded on top of the base Llama model for inference or further fine-tuning.

### 2. Quantized GGUF Models

Quantized **GGUF format models** compatible with:

* Ollama
* llama.cpp

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.md
```


---

# Usage with Ollama

The merged GGUF file can be used with:

* Ollama


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:

```
WILL BE PUBLISHED
```