Fine-tuning & models
Narrow-task specialist
Take one narrow task a big model does slowly and expensively, and fine-tune a tiny open model that matches it while being far cheaper and faster.
Copy or download the full plan and paste it into your AI coding agent to build it.
Why build it
Companies burn money running a huge frontier model on the same simple, repetitive task millions of times. When the task is narrow, like classifying a ticket or extracting fields, a tiny fine-tuned model can match the big one at a fraction of the cost and latency. The trick is to use the expensive model once to generate training data, then distil that behaviour into a small model you own. This distillation skill is exactly what teams hire for, because it turns a recurring API bill into a fixed, self-hosted asset, and it proves you understand the economics of AI in production, not just the demos.
Who it's for: You have fine-tuned before or built serious LLM apps, and you want to show you can cut cost and latency with a measured trade-off. You will need a GPU, but a free Google Colab or Kaggle notebook GPU is enough.
What you'll build
Core (MVP)
- One clearly scoped task, for example intent classification or field extraction
- A dataset generated by a strong model acting as the teacher
- A tiny open model fine-tuned on that dataset (the student)
- A held-out test set with a labelled gold answer for each row
- A comparison table: accuracy, latency, and cost per 1,000 calls, teacher vs student
- The student running self-hosted with no per-call API fee
Stretch
- Quantize the student to 4-bit or 8-bit and re-measure speed and accuracy
- Export to GGUF and benchmark CPU-only inference
- A confidence threshold that escalates hard cases back to the big model
- A small load test showing throughput on the free tier
- A short report on the break-even call volume where the student wins
Step-by-step build
- 1
Scope the task and its metric
Pick one repetitive task where a fixed output is checkable, such as classifying a message into one of five intents. Decide the single metric that defines success, for example accuracy against a gold label. A tight scope is what makes a tiny model competitive.
- 2
Generate data with the teacher
Use a strong model on the Groq free tier to label a few thousand real or realistic inputs, producing the input/output pairs you will train on. Spot-check the teacher's outputs and drop the bad ones, because the student can only be as good as this data. Set aside a clean, human-verified slice as the test set.
- 3
Measure the teacher baseline
Run the teacher on the held-out test set and record accuracy, average latency, and cost per 1,000 calls. These numbers are the bar the student has to reach. Write them down now so the final comparison is honest.
- 4
Fine-tune the student
In a free Colab or Kaggle notebook, load a tiny base model in 4-bit and attach a LoRA adapter, then train it on the teacher-generated data. Keep the model small on purpose; the whole point is speed and cost, not raw capability. Save checkpoints in case the notebook disconnects.
- 5
Evaluate the student head to head
Run the student on the same test set and compare accuracy, latency, and cost against the teacher. If accuracy is close but latency and cost are far lower, you have won. If accuracy lags, improve the training data before changing hyperparameters.
- 6
Add an escalation fallback
Have the student return a confidence signal, and route only the low-confidence cases back to the teacher. This keeps quality high while still handling the easy majority cheaply. Measure what fraction escalates so you can report the true cost.
- 7
Serve the student
Wrap the student in a FastAPI endpoint and deploy it self-hosted, optionally quantized to 4-bit or exported to GGUF for CPU inference. Confirm it answers with no per-call API fee. Run a small load test to record throughput.
- 8
Write the cost report
Produce a clear table of accuracy, latency, and cost per 1,000 calls for teacher vs student, plus the break-even call volume where the student pays for itself. Put it at the top of the README. The economics are the headline, so lead with the numbers.
Done when
- ✓The student matches the teacher's accuracy within a small, reported margin on the held-out test set.
- ✓The student is clearly faster and cheaper per 1,000 calls, with the numbers written down.
- ✓Training ran on a free Colab or Kaggle GPU without running out of memory.
- ✓The self-hosted endpoint answers real inputs with no per-call API fee.
Ship it
Serve the fine-tuned student from a FastAPI service on the AWS free tier or a Hugging Face Space, optionally quantized or exported to GGUF for CPU-only inference. Lead the README with the accuracy, latency, and cost-per-1,000 table comparing teacher and student, and state the break-even call volume. Keep the teacher wired in only as an escalation path for low-confidence cases.
What it proves: You can distil an expensive model into a cheap, fast, self-hosted one and prove the trade-off with real cost and latency numbers. That is exactly the economics teams hire AI engineers to fix.
Hand it to your AI agent
Paste this into Cursor, Claude, or ChatGPT and build it step by step.
You are my senior AI engineer pair. Help me build "Narrow-task specialist" step by step. Stack: Groq free tier as the teacher model that generates labelled training data, a free Google Colab or Kaggle GPU for training, Hugging Face PEFT or Unsloth for LoRA/QLoRA fine-tuning (LoRA/QLoRA = low-rank fine-tuning with small adapter matrices, base model loaded in 4-bit to fit a free GPU), a tiny open base model such as Qwen2.5 1.5B as the student, and FastAPI to serve it. Requirements: 1. One narrow task with a checkable output and a single success metric. 2. Use the teacher to label a few thousand inputs; keep a clean human-verified test set. 3. Record the teacher baseline: accuracy, latency, and cost per 1,000 calls. 4. Fine-tune the tiny student on the teacher data and evaluate it head to head on the same test set. 5. Add a confidence-based escalation to the teacher for hard cases, then serve the student from FastAPI. Work in this order: scope and metric, data generation, teacher baseline, training, evaluation, then serving and the cost report. STOP after each step so I can test. Do not write the whole app at once.
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