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Instructor Alternatives
Similar projects and alternatives to instructor
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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simpleaichat
Python package for easily interfacing with chat apps, with robust features and minimal code complexity.
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hyperdx
Resolve production issues, fast. An open source observability platform unifying session replays, logs, metrics, traces and errors powered by Clickhouse and OpenTelemetry.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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swiss_army_llama
A FastAPI service for semantic text search using precomputed embeddings and advanced similarity measures, with built-in support for various file types through textract.
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PythonGPT
PythonGPT writes and indexes code to implement dynamic code execution using generative models. Younger sibling of DoctorGPT.
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gpu_poor
Calculate token/s & GPU memory requirement for any LLM. Supports llama.cpp/ggml/bnb/QLoRA quantization
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
instructor reviews and mentions
- Instructor: Structured Outputs for LLMs
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Anthropic's Haiku Beats GPT-4 Turbo in Tool Use
Ah yes. Have you tried out instructor [0] or Guidance [1]?
[0]: https://github.com/jxnl/instructor/
- Instructor: Structured Data Like JSON from Large Language Models
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Show HN: Fructose, LLM calls as strongly typed functions
Good stuff. How does this compare to Instructor? I’ve been using this extensively
https://jxnl.github.io/instructor/
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Show HN: Ellipsis – Automatic pull request reviews
it's super cool! checkout how the Instructor repo uses it to keep various parts of their docs in sync: https://github.com/jxnl/instructor/blob/main/ellipsis.yaml
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Pushing ChatGPT's Structured Data Support to Its Limits
I've been using the instructor[1] library recently and have found the abstractions simple and extremely helpful for getting great structured outputs from LLMs with pydantic.
1 https://github.com/jxnl/instructor/tree/main
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Efficiently using python in GPTs
Maybe try using jason liu’s instructor package (https://github.com/jxnl/instructor) to structure the outputs with pydantic? It’s explained in his presentation from the AI Engineer summit (https://youtu.be/yj-wSRJwrrc)
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Ask HN: Cheapest way to run local LLMs?
One of the most powerful ways to integrate LLMs with existing systems is constrained generation. Libraries such as outlines[1] and instructor[2] allow structural specification of the expected outputs as regex patterns, simple types, jsonschema or pydantic models.
These outputs often consume significantly fewer tokens than chat or text completion.
[1] https://github.com/outlines-dev/outlines
[2] https://github.com/jxnl/instructor
- OpenAI Function Calls for Humans
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Unbounded Books: Search by ~Vibes
The best GPT-wrapper you’ll see today?
...but this one hasn't raised oodles of cash.
Mike (creator) here, excited to hear what HN-folks think. Anything to add/improve?
Had fun building, extra s/out to Railway, NextJS, and https://github.com/jxnl/instructor
Check it out: https://www.unboundedbooks.com/
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A note from our sponsor - InfluxDB
www.influxdata.com | 4 May 2024
Stats
jxnl/instructor is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of instructor is Python.
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