初始化项目，由ModelHub XC社区提供模型
Model: arungovindneelan/foam-cfd-unified-7b Source: Original Platform
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README.md
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README.md
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---
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language: en
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license: apache-2.0
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base_model: Qwen/Qwen2.5-Coder-7B-Instruct
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tags:
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- cfd
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- openfoam
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- gmsh
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- fluid-dynamics
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- fine-tuned
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- qwen2.5
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- qlora
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pipeline_tag: text-generation
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---
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# foam-cfd-unified-7B
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A single **Qwen2.5-Coder-7B-Instruct** model fine-tuned for end-to-end CFD workflows —
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covering Gmsh mesh generation, OpenFOAM boundary condition files, and BC JSON patch specs.
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---
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## What this model does
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| Task | Input | Output |
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|---|---|---|
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| **Mesh generation** | Plain-English geometry description | Gmsh `.geo` script |
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| **OpenFOAM case files** | Flow prompt + mesh patches | `0/U`, `0/p`, `0/k`, `0/epsilon` BC files |
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| **BC patch spec** | Flow description + existing BC template | Compact JSON diff applied on top of template |
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**Example prompts:**
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- `"Lid driven cavity, Re=1000, 0.1m x 0.1m"`
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- `"Flow over a cylinder, diameter=0.05m, Re=100"`
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- `"2D backward-facing step, step height 0.05m, Re=500"`
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- `"NACA 0012 airfoil, chord=1m, AoA=5deg, Re=1e6"`
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---
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## Model info
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| Field | Value |
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|---|---|
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| Base model | Qwen2.5-Coder-7B-Instruct |
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| Fine-tuning method | QLoRA (LoRA r=64, alpha=16, 4-bit NF4) via Unsloth |
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| Training examples | 20,505 (mesh-gen + foam-gen + patch-gen + raft-patch-gen) |
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| Validation examples | 2,277 |
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| VRAM required | ~6 GB (4-bit inference) / ~16 GB (full precision) |
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| Sequence length | 4096 tokens |
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---
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## Quickstart — use with the foam-cfd-ai server
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### 1. Clone the deployment repo
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```bash
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git clone https://github.com/AGN000/foam-cfd-deploy
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cd foam-cfd-deploy
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pip install -r requirements.txt
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```
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### 2. Download this model
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```bash
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pip install huggingface_hub
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python3 -c "
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from huggingface_hub import snapshot_download
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snapshot_download(
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'arungovindneelan/foam-cfd-unified-7b',
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local_dir='checkpoints/unified/merged'
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)
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"
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```
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### 3. Build the RAG index
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Requires OpenFOAM 11 tutorials (usually at `/opt/openfoam11/tutorials`):
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```bash
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python3 -m rag.build_index
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```
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### 4. Start the inference server
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```bash
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python3 -m inference.server --model checkpoints/unified/merged --port 8000
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```
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### 5. Run a simulation end-to-end
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```bash
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# Quick demo
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python3 demo.py "Lid driven cavity, Re=1000"
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# Or call the API directly
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curl -X POST http://localhost:8000/simulate \
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-H "Content-Type: application/json" \
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-d '{"prompt": "flow over a cylinder, Re=100, diameter 0.1m"}'
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```
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---
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## Direct Python usage (without the server)
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "arungovindneelan/foam-cfd-unified-7b"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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# Generate a Gmsh mesh script
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messages = [
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{"role": "system", "content": "You are a CFD mesh generation expert. Generate valid Gmsh .geo scripts."},
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{"role": "user", "content": "Create a 2D lid-driven cavity mesh, 0.1m x 0.1m, structured 50x50 grid."},
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.1, do_sample=True)
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response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
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print(response)
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```
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---
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## System requirements
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- Linux (Ubuntu 20.04+)
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- Python 3.10+
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- NVIDIA GPU with >= 8 GB VRAM (tested on H100, A100, RTX 3090)
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- CUDA 12.x
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- OpenFOAM 11 (for simulation — not required for model inference alone)
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---
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## Repository structure
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```
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foam-cfd-ai/
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checkpoints/unified/merged/ <- this model
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inference/
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server.py <- FastAPI server (POST /generate /mesh /simulate)
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mesh_pipeline.py <- Gmsh script generation + validation
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rag/
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build_index.py <- build vector store from OpenFOAM tutorials
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llm_case_generator.py <- LLM-driven BC file generation
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rag_case_builder.py <- RAG + LLM -> OpenFOAM case directory
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retriever.py <- vector search over tutorial chunks
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simulation/
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case_builder.py <- hardcoded fallback case builder
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foam_runner.py <- runs foamRun -solver incompressibleFluid
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training/
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train.py <- QLoRA fine-tuning (Unsloth + TRL)
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config_unified.yaml <- training config for this model
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demo.py <- end-to-end demo script
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requirements.txt
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```
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---
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## API endpoints (when server is running)
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| Endpoint | Method | Description |
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| `/health` | GET | Server health check |
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| `/generate` | POST | Generate Gmsh `.geo` script from prompt |
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| `/mesh` | POST | Generate + validate mesh, return stats |
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| `/simulate` | POST | Full pipeline: mesh → OpenFOAM case → run → results |
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### Example: `/simulate`
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```bash
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curl -X POST http://localhost:8000/simulate \
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-H "Content-Type: application/json" \
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-d '{
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"prompt": "NACA 0012 airfoil, chord 1m, AoA 5 degrees, Re 1e6",
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"n_iter": 500
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}'
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```
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Response:
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```json
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{
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"status": "converged",
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"solver": "simpleFoam",
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"iterations": 487,
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"residuals": {
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"Ux": {"initial": 1.0, "final": 3.2e-5},
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"Uy": {"initial": 1.0, "final": 8.7e-5},
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"p": {"initial": 1.0, "final": 2.1e-4}
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},
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"case_dir": "/tmp/foam_cases/20260407_094950_airfoil"
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}
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```
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---
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## Training data breakdown
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| Task | Examples | Description |
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| mesh-gen | ~5,000 | Gmsh `.geo` script generation |
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| foam-gen | ~5,000 | Full OpenFOAM BC file generation |
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| patch-gen | ~5,000 | JSON BC patch spec generation |
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| raft-patch-gen | ~5,505 | RAFT-style retrieval-augmented patch generation |
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| **Total** | **20,505** | Unified training set |
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---
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## License
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Apache 2.0 — same as the Qwen2.5-Coder base model.
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