TuningFork Information

TuningFork is the managed fine-tuning platform built for developers shipping agentic AI. Most fine-tuning tools assume you have an AI/ML infrastructure team, weeks to spare, and a way to prove the model you trained is actually better than the one you started with. TuningFork removes all three. Drop in a JSONL, JSON, or CSV. We auto-detect whether it's function-calling, chat, or instruction-response data and structure it into the right chat template for your chosen base model (currently available: Llama 3.1 8B, Qwen 2.5 7B, Mistral 7B, Llama 3.2 3B). A recommendation engine picks the right model for your dataset. One click kicks off LoRA fine-tuning on spot GPUs (60–91% cheaper than on-demand) with live loss, learning rate, and GPU-utilization streamed to your dashboard. Then the part no one else does: every training run is automatically benchmarked three ways: your base model vs. your fine-tuned model vs. a strong reference on function-call accuracy, JSON parse rate, parameter accuracy, and hallucination rate. You get proof that your fine-tune moved the needle before you ship it. Deploy to an OpenAI-compatible vLLM endpoint with dynamic LoRA loading, push to Hugging Face, convert to GGUF for Ollama and LM Studio, deploy to Cloud Run, or just download the weights. No vendor lock-in. Everything is REST API-first. The dashboard is one client of the API. Embed fine-tuning directly into your CI/CD, your eval harness, or an agent's self-improvement loop. What's different: 1. Three-way comparative evaluation. Unique in the category. 2. Function-calling-first metrics (BFCL-derived). 3. Auto data structuring + base-model recommendation. 4. Spot-GPU economics with checkpoint-resumable training. 5. Open-weight models and multi-format export. Status: TuningFork has not started Alpha Testing yet. We are recruiting our first cohort of developers building agents, coding assistants, and CI/CD automation.