✨ Sometimes you just want to throw a large file at a model and ask: “Summarise this without losing the good bits.” Then reality appears. 🫠 Local models often do not have giant context windows. 🌱 Smaller, cheaper, more eco-friendly cloud models also often do not have giant context windows. So instead of pretending one huge file will fit cleanly, this little toolkit does the sensible thing: ✂️ split the file into overlapping chunks 🤖 summarise each chunk with either Ollama or cloud models 🧵 stitch the chunk summaries back together Simple. Reusable. No drama. gist.github.com/simbo1905/053b482... Fetch it with: for f in $(gh gist view 053b48269b1e95800500b85190adf427 --files); do gh gist view 053b48269b1e95800500b85190adf427 -f "$f" > "$f"; done && chmod +x *.py *.awk chunk_text.awk process_chunks_cloud.py process_chunks_ollama.py Because smaller models are not magic. If your source is too big, they either: miss details flatten nuance hallucinate structure or just do a bad job So chunk_text.awk creates overlapping chunks. Default settings: size: 11000 characters step: 10000 characters overlap: 1000 characters That overlap is there on purpose so ideas near a chunk boundary do not get chopped in half and quietly vanish into the void. ☠️ awk -v size=11000 -v step=10000 -f chunk_text.awk notes.md You’ll get files like: notes00.md notes01.md notes02.md uv run process_chunks_ollama.py example_chunks transcript summary Default local model: gemma4:26b It accepts either: transcript00.md transcript00.log style chunk files. uv run process_chunks_cloud.py example_chunks transcript summary The script checks for API keys in this order: shell MISTRAL_API_KEY shell GROQ_API_KEY repo-root .env MISTRAL_API_KEY repo-root .env GROQ_API_KEY It is trivial to add your own API key, yet those are small, fast, open models. Yes, obviously, you can change the prompt. That is the whole point. Both summariser scripts support -p/--prompt. Example: uv run process_chunks_cloud.py example_chunks transcript summary -p "Summarise the argument, key evidence, and open questions." Transcript-style prompt shape used in testing: This is a transcript of an only video. The title is: ${title}. The transcript format is 'Speaker Name, timestamp, what they said'. Summarise the content in a terse, business-like, action-oriented way. Preserve substantive points, facts, figures, citations, and practical recommendations. Do not be chatty. For each input chunk, the scripts write: summary_transcript00.md thinking_summary_transcript00.md The thinking_ file keeps the raw model output. The clean summary file strips thinking blocks where possible. cat summary_transcript*.md > all_summary.md If there is a bit of overlap or repetition due to overlapping junk, so that split-up ideas are not lost. YMMV. Update process_chunks_cloud.py in these places: add the key lookup and its priority order add the approved model names / aliases add the provider request function route the models to that provider in call_model Happy chunking. Happy summarising. May your small models punch above their weight. 🚀