language, license, base_model, tags, pipeline_tag

language	license	base_model	tags	pipeline_tag
en	apache-2.0	Qwen/Qwen2.5-Coder-7B-Instruct	cfd openfoam gmsh fluid-dynamics fine-tuned qwen2.5 qlora	text-generation

foam-cfd-unified-7B

A single Qwen2.5-Coder-7B-Instruct model fine-tuned for end-to-end CFD workflows — covering Gmsh mesh generation, OpenFOAM boundary condition files, and BC JSON patch specs.

---

What this model does

Task	Input	Output
Mesh generation	Plain-English geometry description	Gmsh .geo script
OpenFOAM case files	Flow prompt + mesh patches	0/U, 0/p, 0/k, 0/epsilon BC files
BC patch spec	Flow description + existing BC template	Compact JSON diff applied on top of template

Example prompts:

• "Lid driven cavity, Re=1000, 0.1m x 0.1m"
• "Flow over a cylinder, diameter=0.05m, Re=100"
• "2D backward-facing step, step height 0.05m, Re=500"
• "NACA 0012 airfoil, chord=1m, AoA=5deg, Re=1e6"

---

Model info

Field	Value
Base model	Qwen2.5-Coder-7B-Instruct
Fine-tuning method	QLoRA (LoRA r=64, alpha=16, 4-bit NF4) via Unsloth
Training examples	20,505 (mesh-gen + foam-gen + patch-gen + raft-patch-gen)
Validation examples	2,277
VRAM required	~6 GB (4-bit inference) / ~16 GB (full precision)
Sequence length	4096 tokens

---

Quickstart — use with the foam-cfd-ai server

1. Clone the deployment repo

git clone https://github.com/AGN000/foam-cfd-deploy
cd foam-cfd-deploy
pip install -r requirements.txt

2. Download this model

pip install huggingface_hub
python3 -c "
from huggingface_hub import snapshot_download
snapshot_download(
    'arungovindneelan/foam-cfd-unified-7b',
    local_dir='checkpoints/unified/merged'
)
"

3. Build the RAG index

Requires OpenFOAM 11 tutorials (usually at /opt/openfoam11/tutorials):

python3 -m rag.build_index

4. Start the inference server

python3 -m inference.server --model checkpoints/unified/merged --port 8000

5. Run a simulation end-to-end

# Quick demo
python3 demo.py "Lid driven cavity, Re=1000"

# Or call the API directly
curl -X POST http://localhost:8000/simulate \
  -H "Content-Type: application/json" \
  -d '{"prompt": "flow over a cylinder, Re=100, diameter 0.1m"}'

---

Direct Python usage (without the server)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "arungovindneelan/foam-cfd-unified-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)

# Generate a Gmsh mesh script
messages = [
    {"role": "system", "content": "You are a CFD mesh generation expert. Generate valid Gmsh .geo scripts."},
    {"role": "user", "content": "Create a 2D lid-driven cavity mesh, 0.1m x 0.1m, structured 50x50 grid."},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.1, do_sample=True)

response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)

---

System requirements

• Linux (Ubuntu 20.04+)
• Python 3.10+
• NVIDIA GPU with >= 8 GB VRAM (tested on H100, A100, RTX 3090)
• CUDA 12.x
• OpenFOAM 11 (for simulation — not required for model inference alone)

---

Repository structure

foam-cfd-ai/
  checkpoints/unified/merged/   <- this model
  inference/
    server.py                   <- FastAPI server (POST /generate /mesh /simulate)
    mesh_pipeline.py            <- Gmsh script generation + validation
  rag/
    build_index.py              <- build vector store from OpenFOAM tutorials
    llm_case_generator.py       <- LLM-driven BC file generation
    rag_case_builder.py         <- RAG + LLM -> OpenFOAM case directory
    retriever.py                <- vector search over tutorial chunks
  simulation/
    case_builder.py             <- hardcoded fallback case builder
    foam_runner.py              <- runs foamRun -solver incompressibleFluid
  training/
    train.py                    <- QLoRA fine-tuning (Unsloth + TRL)
    config_unified.yaml         <- training config for this model
  demo.py                       <- end-to-end demo script
  requirements.txt

---

API endpoints (when server is running)

Endpoint	Method	Description
/health	GET	Server health check
/generate	POST	Generate Gmsh .geo script from prompt
/mesh	POST	Generate + validate mesh, return stats
/simulate	POST	Full pipeline: mesh → OpenFOAM case → run → results

Example: /simulate

curl -X POST http://localhost:8000/simulate \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "NACA 0012 airfoil, chord 1m, AoA 5 degrees, Re 1e6",
    "n_iter": 500
  }'

Response:

{
  "status": "converged",
  "solver": "simpleFoam",
  "iterations": 487,
  "residuals": {
    "Ux": {"initial": 1.0, "final": 3.2e-5},
    "Uy": {"initial": 1.0, "final": 8.7e-5},
    "p":  {"initial": 1.0, "final": 2.1e-4}
  },
  "case_dir": "/tmp/foam_cases/20260407_094950_airfoil"
}

---

Training data breakdown

Task	Examples	Description
mesh-gen	~5,000	Gmsh .geo script generation
foam-gen	~5,000	Full OpenFOAM BC file generation
patch-gen	~5,000	JSON BC patch spec generation
raft-patch-gen	~5,505	RAFT-style retrieval-augmented patch generation
Total	20,505	Unified training set

---

License

Apache 2.0 — same as the Qwen2.5-Coder base model.