10 GitHub repos for building an AI engineering lab with GPT-6 Astra
A GitHub list that chains Astra into a pipeline for generating data, fine-tuning, and evaluating and deploying models to improve them automatically.
AI agents are getting f...cking good at building other AI agents 10 GitHub repos for turning GPT 6 Astra into an AI engineering lab that generates training data, improves models, and tests what actually works 01 DSPy ▸ https://github.com/stanfordnlp/dspy → optimize prompts and AI pipelines programmatically GENERATE THE TRAINING DATA 02 Distilabel ▸ https://github.com/argilla-io/distilabel → generate and process synthetic datasets with LLMs 03 DataDreamer ▸ https://github.com/datadreamer-dev/DataDreamer → build reproducible synthetic data workflows 04 Argilla ▸ https://github.com/argilla-io/argilla → collect human feedback and curate datasets TRAIN THE SPECIALISTS 05 Unsloth ▸ https://github.com/unslothai/unsloth → accelerate fine tuning with optimized training workflows 06 LLaMA Factory ▸ https://github.com/hiyouga/LLaMA-Factory → fine tune and evaluate open models 07 Axolotl ▸ https://github.com/axolotl-ai-cloud/axolotl → configure and run model training pipelines TEST WHAT ACTUALLY IMPROVED 08 lm-evaluation-harness ▸ https://github.com/EleutherAI/lm-evaluation-harness → benchmark models across standardized tasks 09 OpenCompass ▸ https://github.com/open-compass/opencompass → evaluate model capabilities across multiple benchmarks DEPLOY THE WINNERS 10 BentoML ▸ https://github.com/bentoml/BentoML → package and serve models as production services the architecture: GPT 6 Astra → generate data → curate → fine tune → benchmark → compare → deploy → collect feedback → repeat 3 builds worth bookmarking: coding specialist: DSPy → Distilabel → Unsloth → lm-evaluation-harness research specialist: DataDreamer → Argilla → LLaMA Factory → OpenCompass production lab: Distilabel → Axolotl → lm-evaluation-harness → BentoML here's the part most people miss you don't need your most powerful model answering every question you can use it to help create the data and workflows that make smaller models useful for specific jobs one powerful model helping build an entire fleet of specialists that's a much more interesting use of intelligence ⭣
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