Choukrijer/buildwellai-model-v2

BuildwellAI Model V2

Fine-tuned Qwen3-14B for UK construction industry applications.

Features

• 42 MCP Server Integration: Tool calling for all BuildwellAI calculation servers
• Multi-Mode Responses: Direct answers, thinking mode, and tool calling
• Anti-Overfitting: Early stopping, dropout, weight decay, validation monitoring
• Streaming API: OpenAI-compatible streaming inference server

Quick Start

1. Prepare Dataset

cd /opt/buildwellai/buildwellai-llm-models/buildwellai-model-v2/scripts
python3 prepare_dataset.py

This will:

• Convert CSV files (BSI, UK Benchmark, Q&A pairs)
• Load existing JSONL datasets (thinking_mode, tool_calling)
• Generate MCP training data for all 42 servers
• Validate and deduplicate
• Split into train/validation sets

2. Fine-Tune

python3 finetune.py

Or with custom config:

python3 finetune.py --config ../configs/training_config.json

3. Run Streaming API

python3 streaming_api.py --model ../output/buildwellai-qwen3-14b-v2/merged --port 8080

Or interactive CLI:

python3 streaming_api.py --model ../output/buildwellai-qwen3-14b-v2/merged --cli

Dataset Sources

Source	Description	Count
qa-buildwell-ai.csv	Q&A pairs	~39K
UK Building Control Benchmark	Building control questions	~160
BSI Flex 8670	Building safety competence	~47
dataset_thinking_mode.jsonl	Reasoning examples	10K
dataset_tool_calling.jsonl	Tool call examples	8K
MCP Generated	All 42 MCP servers	~250

Anti-Overfitting Measures

1. Lower Learning Rate: 1e-5 (vs typical 2e-4)
2. Weight Decay: 0.05 L2 regularization
3. LoRA Dropout: 0.1 in adapter layers
4. Early Stopping: Patience of 3, monitors val_loss
5. Validation Split: 5% held out for monitoring
6. Lower LoRA Rank: r=16 reduces capacity
7. Fewer Epochs: 2 epochs max
8. Gradient Clipping: max_grad_norm=0.5

Estimated Training Time

On 2x RTX A5000 (RunPod):

Dataset Size	Method	Time	Cost
~60K samples	Unsloth QLoRA	~12-15 hours	~$13-16
~60K samples	HF Standard	~25-30 hours	~$25-30

API Usage

OpenAI-Compatible Endpoint

import openai

client = openai.OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="not-needed"
)

response = client.chat.completions.create(
    model="buildwellai-qwen3-14b-v2",
    messages=[
        {"role": "user", "content": "What are the PSI values for junction E5?"}
    ],
    stream=True
)

for chunk in response:
    print(chunk.choices[0].delta.content, end="")

Direct Endpoint

curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [{"role": "user", "content": "Calculate U-value for cavity wall"}],
    "stream": false
  }'

File Structure

buildwellai-model-v2/
├── configs/
│   └── training_config.json
├── datasets/
│   ├── train.jsonl
│   ├── validation.jsonl
│   └── dataset_stats.json
├── scripts/
│   ├── prepare_dataset.py
│   ├── finetune.py
│   └── streaming_api.py
├── output/
│   └── buildwellai-qwen3-14b-v2/
│       ├── adapter/
│       └── merged/
└── logs/

MCP Servers Covered

All 42 BuildwellAI MCP calculation servers:

• Structural: Part A, disproportionate collapse
• Thermal: U-value, PSI, condensation, thermal break
• Energy: SAP10, SBEM, Part L, Passivhaus
• Fire: Safety, smoke ventilation, evacuation
• Sustainability: BREEAM, WELL, LCA, embodied carbon
• Water: Part G, drainage, SuDS, flood risk
• Comfort: Daylight, overheating, acoustics, ventilation
• And more...