The Foundry Creation Studio
The Foundry is the custom model development and fine-tuning suite integrated directly into Howitzer. It provides a complete, sovereign pipeline from unstructured raw documentation to a fine-tuned, quantized, edge-deployable model.
1. Agentic Synthetic Data Synthesis
Building high-quality instruction datasets typically requires expensive human labeling. The Foundry automates this with multi-agent adversarial synthesis:
- Seed Ingestion: Point the synthesizer to a directory of Markdown documentation, C++/Rust source code, or legal/technical PDFs.
- Generator Agent: Reads chunked context and synthesizes challenging questions, refactoring tasks, or reasoning queries.
- Critic Agent: Performs adversarial scrutiny, checking for factual hallucinations, syntax errors, and tone alignment.
- Rejection Sampling: Discards low-scoring samples automatically. Only top-tier instruction pairs are retained.
- Dataset Export: Outputs clean, verified datasets in
JSONL,Alpaca,ShareGPT, orParquetformats.
2. Desktop LoRA & QLoRA Fine-Tuning
Fine-tune open-weight models (such as Llama 3.3 8B or Qwen 2.5 Coder 7B) directly on your Apple Silicon Mac using Metal Performance Shaders (MPS):
[EPOCH 2/3] Step 680/1200 | Loss: 0.412 | Perplexity: 1.51 | LR: 1.8e-5 | VRAM: 9.4GB
[CHECKPOINT] Saved adapter weights to ~/.howitzer/adapters/custom_lora_v1.safetensors
Hyperparameters are configured visually in the Foundry UI or via the CLI: LoRA rank (r=8..64), scaling alpha (16..128), target linear modules (q_proj, v_proj, k_proj, o_proj), and gradient accumulation steps.
3. 1-Click Quantization to GGUF & MLX
Once training completes, you can merge the LoRA weights back into the base model or save them as a dynamic standalone adapter. With 1-click GGUF quantization, the Foundry packages your model into 4-bit (Q4_K_M) or 8-bit (Q8_0) formats, running automated perplexity verification to assert that precision loss remains under 1.5%.
4. Live Canary Range & Regression Testing
Before deploying a custom model into production workflows, you must verify that it hasn't suffered from catastrophic forgetting of core logic:
- Launch the Canary Range to mount your custom adapter alongside the base model and a frontier baseline (such as Claude 3.5 Sonnet).
- Execute automated regression benchmark suites (GSM8k, HumanEval, or custom internal test sets).
- Expose your fine-tuned model instantly as a local, OpenAI-compatible HTTP endpoint at
http://127.0.0.1:8080/v1/chat/completions.