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4 use cases
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Post-train a base model on a math task overnight
Run the bundled GSM8K example to turn Qwen3-1.7B-Base into a grade-school math solver.
View prompt
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.
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Post-train a model for your own task and data
Point a run at a folder with your task statement and data files, then start post-training.
View prompt
Using the aibuildai-llm-posttrain-agent repo, create a task.yaml with run.data_root set to <task_folder>, then run `aibuildai run task.yaml`. Expected output: a deliverable directory and a score program under private/. Verify that the score program exists and reads the task folder before the run starts.
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Grade a post-trained model with the task's own grader
Score a deliverable model with the frozen grader that the run trained against.
View prompt
For the aibuildai-llm-posttrain-agent GSM8K example, run the grader in the run's Program environment against deliverable/final_model with --limit -1. Expected output: a grader score for the full test split. Verify that the model path exists and that the score is reported on the full split before stating the result.
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Carry lessons from past runs into a new run
Fold earlier runs of a task into an editable memory document and inject it into new runs.
View prompt
In the aibuildai-llm-posttrain-agent repo, run `aibuildai memorize task.yaml`, then set memory.enable to true and run `aibuildai run task.yaml`. Expected output: a memory document and a new run that uses it. Verify that memory.enable is true in the config before starting the run.
Quick start with Aibuildai LLM Posttrain Agent
- Check you have what it needs: 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.
- Open aibuildai-inc/aibuildai-llm-posttrain-agent on GitHub and read the README and license.
- Confirm the requirements, then run
python -m venv .venv && source .venv/bin/activate - Run
pip install -r requirements.lock && pip install -e . --no-deps - Authenticate with
claude auth login, or runexport AIBUILDAI_API_KEY=sk-ant-... - Build the example task with
bash examples/gsm8k-qwen3-1.7b-base/setup.sh ~/gsm8k-example
These steps come from our top use case, Post-train a base model on a math task overnight, which also has a copy-paste agent prompt.
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About this repo
- Who it is forML engineers and small teams with a Linux GPU host who want an agent to post-train a model against their own task and score.
- Why it is hot110 stars, launched 38 days ago
- LanguagePython
- Sourcegithub.com/aibuildai-inc/aibuildai-llm-posttrain-agent · Back to Hot repos
Requirements
- 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
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Questions about Aibuildai LLM Posttrain Agent
What is Aibuildai LLM Posttrain Agent?
A source-code release of a recursive self-improving agent that designs, runs, and iterates on post-training experiments for LLMs. It is an open-source project on GitHub: https://github.com/aibuildai-inc/aibuildai-llm-posttrain-agent.
Who is Aibuildai LLM Posttrain Agent for?
ML engineers and small teams with a Linux GPU host who want an agent to post-train a model against their own task and score.
What can I do with Aibuildai LLM Posttrain Agent?
4 use cases so far, for example: Post-train a base model on a math task overnight; Post-train a model for your own task and data; Grade a post-trained model with the task's own grader. Each has steps and a copy-paste agent prompt.
What do I need to run Aibuildai LLM Posttrain Agent?
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.
What are alternatives to Aibuildai LLM Posttrain Agent?
Other hot AI repos in Software and DevOps: Codex LB Enhanced, Reuse Before Build, FlowX, Codex Guard, Kyb.
Is Aibuildai LLM Posttrain Agent safe to run?
Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.