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Scout Monitoring
Free Django app performance insights with Scout Monitoring. Get Scout setup in minutes, and let us sweat the small stuff. A couple lines in settings.py is all you need to start monitoring your apps. Sign up for our free tier today.
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segment-anything
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
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txtai
💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
On https://flowchart.fun I found that I got better overall results by asking GPT for an intermediate syntax that it was less likely to mess up (and easier for me to parse), and then parsing and transforming that syntax to my DSL. The relevant code: https://github.com/tone-row/flowchart-fun/blob/main/api/prom...
There are a couple different approaches:
- Use multi-shot prompting with something like guardrails to try prompting a commercial model until it works. [1]
- Use a local model with something with a final layer that steers token selection towards syntactically valid tokens [2]
[1] https://github.com/ShreyaR/guardrails
[2] "Structural Alignment: Modifying Transformers (like GPT) to Follow a JSON Schema" @ https://github.com/newhouseb/clownfish.
Have you looked at https://github.com/facebookresearch/segment-anything ?
txtai accomplished a similar task by fine tuning a very small t5 model, notebook with usage samples (training code has to be somewhere near)
https://github.com/neuml/txtai/blob/master/examples/33_Query...