We took Qwen 2.5-7B — a free, open-source model anyone can download — and trained it to reason: to understand cause and effect, distinguish correlation from causation, and think through what would happen if you changed something. Run the benchmark yourself.
| Model | Score | Accuracy | Notes |
|---|---|---|---|
| TunedAI Labs — Qwen 2.5-7B | 96.96% | 7B params · open-source · fine-tuned | |
| GPT-4o | ~72% | General purpose · closed source | |
| Claude 3.5 Sonnet | ~68% | General purpose · closed source | |
| Base Qwen 2.5-7B | ~62% | Same model · no fine-tuning |
CLadder benchmark — 10,000+ questions. Public. Independently runnable. Verify it yourself →
We take an existing open-source model and train it to reason in your domain — not just to score better on tests, but to understand cause and effect, ask the right questions, and know when a conclusion actually follows from the evidence. You own the result forever.
We benchmark your current model against your actual task. No guessing — you see exactly where it's underperforming before we touch anything.
We curate high-quality examples of the task done correctly. This is where most fine-tuning fails — we specialize in training data that teaches reasoning, not surface-level pattern matching.
We train the model specifically to reason — not just to pattern-match answers. Then we measure reasoning performance on a held-out benchmark. You get before and after numbers, not just our word that it improved.
Every API call to a closed-source model is a recurring bill you don't control, on servers you don't own, with a provider who can change pricing or go down anytime. A fine-tuned open-source model eliminates all of that.
A fine-tuned 7B model on your own hardware costs a fraction of a GPT-4o API call. At scale, the difference is orders of magnitude.
No API call means no data leaving your environment. Critical for healthcare, legal, finance, and defense where data cannot touch a third-party server.
OpenAI deprecates models. Anthropic changes pricing. A fine-tuned model you own runs the same way forever, regardless of what any vendor decides.
A general model pattern-matches. A reasoning-trained model understands cause and effect in your domain — why a diagnosis follows from symptoms, why a clause creates liability, why a signal indicates a threat. The benchmark above shows what that difference looks like.
Cost estimate at 1M queries/month. Closed API based on GPT-4o pricing. Open-source assumes A100 cloud GPU amortized over 24 months.
We work with teams whose models need to reason — not just retrieve or rephrase, but actually understand cause and effect in their domain. General-purpose models weren't built for that. We are.
We train an open-source model to reason in your domain — legal, medical, financial, security, or any field where the model needs to understand cause and effect, not just match patterns. You get the weights and run the model anywhere.
Most PopularWe design a benchmark for your task and measure performance before and after. You get a verifiable number — not a vague claim of "improvement."
Always IncludedMost fine-tuning fails because of bad training data. We build the dataset that actually teaches reasoning — not surface-level pattern matching. This is the hard part.
The Hard PartWe help you deploy the fine-tuned model in your environment — on-premise, private cloud, or air-gapped. We write the inference API and integrate with your stack.
Optional Add-OnFine-tuning is not a one-time event. As your use case evolves, we retrain with new data and measure the delta. Ongoing relationship, not a one-off project.
Retainer OptionIf you have a novel reasoning problem — something no existing model handles well — we research and develop a fine-tuning approach from scratch. We publish when appropriate.
Custom ScopeWe'll tell you whether reasoning fine-tuning can fix it, which model to start from, and what a realistic benchmark score looks like. No commitment — just an honest conversation about whether we can actually help.
Or email us directly: hello@tunedailabs.com