awesome-data-temporality
Llama-2-Onnx
awesome-data-temporality | Llama-2-Onnx | |
---|---|---|
17 | 3 | |
96 | 987 | |
- | 2.0% | |
10.0 | 6.7 | |
over 1 year ago | 4 months ago | |
Python | ||
- | GNU General Public License v3.0 or later |
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awesome-data-temporality
- FLaNK Stack Weekly for 14 Aug 2023
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Easy, alternative soft deletion: `deleted_record_insert`
I’ve recently put together an awesome list about temporality, including: soft delete, time travel, slowly changing dimensions, and bitemporality. https://github.com/daefresh/awesome-data-temporality
- Show HN: List of Data Time Travel
- List: Everything Data Temporality
- “Awesome” list I made: Data Temporality 🚀 (r/DataScience)
- “Awesome” list I made: data temporality 🚀
- “Awesome” list I made: Data Temporality 🚀
Llama-2-Onnx
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Show HN: Fine-tune your own Llama 2 to replace GPT-3.5/4
System: Here's some docs, answer concisely in a sentence.
YMMV on cost still, depends on cloud vendor, and my intuition & viewpoint agrees with yours, GPT-3.5 is priced low enough that there isn't a case where it makes sense to use another model.
It strikes me now that _very_ likely and not just our intuition: OpenAI's $/GPU hour is likely <= any other vendor's.
The next big step will come from formalizing the stuff rolling around the local LLM community, for months now it's either been one-off $X.c stunts that run on desktop, and the vast majority of the _actual_ usage and progress is coming from porn-y stuff, like all nascent tech.
Microsoft has LLaMa-2 ONNX available on GitHub[1]. There's budding but very small projects in different languages to wrap ONNX. Once there's a genuine cross-platform[2] ONNX wrapper that makes running LLaMa-2 easy, there will be a step change. It'll be "free"[3] to run your fine-tuned model that does as well as GPT-4 .
It's not clear to me exactly when this will occur. It's "difficult" now, but only because the _actual usage_ in the local LLM community doesn't have a reason to invest in ONNX, and it's extremely intimidating to figure out how exactly to get LLaMa-2 running in ONNX. Microsoft kinda threw it up on GitHub and moved on, the sample code even still needs a PyTorch model. I see at least one very small company on HuggingFace that _may_ have figured out full ONNX.
[1] https://github.com/microsoft/Llama-2-Onnx
- FLaNK Stack Weekly for 14 Aug 2023
- Llama 2 on ONNX runs locally
What are some alternatives?
jdbc-connector-for-apache-kafka - Aiven's JDBC Sink and Source Connectors for Apache Kafka®
vllm - A high-throughput and memory-efficient inference and serving engine for LLMs
sqlalchemy-easy-softdelete - Easily add soft-deletion to your SQLAlchemy Models
pkgx - the last thing you’ll install
sql-cli-for-apache-flink-docker - SQL CLI for Apache Flink® via docker-compose
onnx-coreml - ONNX to Core ML Converter
databathing
OpenPipe - Turn expensive prompts into cheap fine-tuned models
rift - Rift: an AI-native language server for your personal AI software engineer
llama.cpp - LLM inference in C/C++
CML_AMP_Churn_Prediction_mlflow - Build an scikit-learn model to predict churn using customer telco data.
gpt-llm-trainer