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How to post-train a base model on a math task overnight with Aibuildai LLM Posttrain Agent

Run the bundled GSM8K example to turn Qwen3-1.7B-Base into a grade-school math solver.

Who it is forAn ML engineer with one GPU who wants a working end-to-end post-training run before adapting the tool to their own data.

The problemSetting up an autonomous post-training pipeline from scratch is slow, and it is hard to tell whether the agent works on a real task.

The outcomeA merged model directory (deliverable/final_model) trained against the GSM8K grader, plus a live workspace view of the run.

What you need first

  • Linux x86_64 with a systemd user session and Python 3.11 to 3.13
  • A C toolchain (gcc, make) with zlib and readline headers, plus bwrap (bubblewrap) with unprivileged user namespaces enabled
  • A Claude Code login or an Anthropic API key, or an Anthropic-compatible endpoint and its key
  • A CUDA GPU is recommended
  • Network access to GitHub releases and ftp.postgresql.org

Steps

  1. Confirm the requirements, then run python -m venv .venv && source .venv/bin/activate
  2. Run pip install -r requirements.lock && pip install -e . --no-deps
  3. Authenticate with claude auth login, or run export AIBUILDAI_API_KEY=sk-ant-...
  4. Build the example task with bash examples/gsm8k-qwen3-1.7b-base/setup.sh ~/gsm8k-example
  5. Start the run with aibuildai run ~/gsm8k-example/task.yaml and open the printed workspace URL

Prompt for your agent

In the aibuildai-llm-posttrain-agent repo, run the GSM8K example setup and then `aibuildai run` on the generated task.yaml. Expected output: a deliverable/final_model directory under the run home. Verify by confirming the run_config.json and deliverable folders exist before reporting success.

Caveat. The README says the example takes about two hours on one GPU and the setup downloads about 4 GB, so the first run depends heavily on your hardware and network.

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Using the Deduped MCP, get the setup steps for aibuildai-inc/aibuildai-llm-posttrain-agent and "Post-train a base model on a math task overnight"

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