kudu VS llama2.c

https://github.com/apache/kudu/blob/master/examples/quickstart/impala/README.adoc https://medium.com/@nifi.notes/building-an-effective-nifi-flow-replacetext-60a6016d378c https://community.cloudera.com/t5/Community-Articles/Running-DNS-and-Domain-Scanning-Tools-From-Apache-NiFi/ta-p/248484 https://community.cloudera.com/t5/Community-Articles/Using-Cloudera-Data-Science-Workbench-with-Apache-NiFi-and/ta-p/249469 https://community.cloudera.com/t5/Community-Articles/Scanning-Documents-into-Data-Lakes-via-Tesseract-MQTT-Python/ta-p/248492 https://community.cloudera.com/t5/Community-Articles/Adding-Stanford-CoreNLP-To-Big-Data-Pipelines-Apache-NiFi-1/ta-p/249378 https://community.cloudera.com/t5/Community-Articles/Using-Apache-NiFi-for-Speech-Processing-Speech-to-Text-with/ta-p/249242 https://community.cloudera.com/t5/Community-Articles/Ingesting-Flight-Data-ADS-B-USB-Receiver-with-Apache-NiFi-1/ta-p/247940 https://community.cloudera.com/t5/Community-Articles/Integrating-lucene-geo-gazetteer-For-Geo-Parsing-with-Apache/ta-p/247993 https://community.cloudera.com/t5/Community-Articles/Creating-WordClouds-From-DataFlows-with-Apache-NiFi-and/ta-p/246605 https://community.cloudera.com/t5/Community-Articles/NIFI-1-x-For-Automatic-Music-Playing-Pipelines/ta-p/247994 https://community.cloudera.com/t5/Community-Articles/Using-Apache-NiFi-with-Apache-MXNet-GluonCV-for-YOLO-3-Deep/ta-p/248979 https://community.cloudera.com/t5/Community-Articles/Tracking-Air-Quality-with-HDP-and-HDF-Part-1-Apache-NiFi/ta-p/248265 https://community.cloudera.com/t5/Community-Articles/Monitoring-Energy-Usage-Utilizing-Apache-NiFi-Python-Apache/ta-p/247525 https://community.cloudera.com/t5/Community-Articles/Using-Command-Line-Security-Tools-from-Apache-NiFi/ta-p/248158 https://community.cloudera.com/t5/Community-Articles/Apache-NiFi-Processor-for-Apache-MXNet-SSD-Single-Shot/ta-p/249240 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https://community.cloudera.com/t5/Community-Articles/Basic-Image-Processing-and-Linux-Utilities-As-Part-of-a-Big/ta-p/249121 https://community.cloudera.com/t5/Community-Articles/Hosting-and-Ingesting-Data-From-Web-Pages-Desktop-and-Mobile/ta-p/244575 https://community.cloudera.com/t5/Community-Articles/QADCDC-Our-how-to-ingest-some-database-tables-to-Hadoop-Very/ta-p/245229 https://community.cloudera.com/t5/Community-Articles/Tracking-Air-Quality-with-HDP-and-HDF-Part-2-Indoor-Air/ta-p/249471 https://community.cloudera.com/t5/Community-Articles/Streaming-Ingest-of-Google-Sheets-with-HDF-2-0/ta-p/247764 https://community.cloudera.com/t5/Community-Articles/Ingesting-Golden-Gate-Records-From-Apache-Kafka-and/ta-p/247557 https://community.cloudera.com/t5/Community-Articles/Data-Processing-Pipeline-Parsing-PDFs-and-Identifying-Names/ta-p/249105 https://community.cloudera.com/t5/Community-Articles/Using-A-TensorFlow-quot-Person-Blocker-quot-With-Apache-NiFi/ta-p/248141 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Really interested in partial updates, but haven't found any information on how physically the merges/upserts happen. It would be great if a doc like https://github.com/apache/kudu/blob/master/docs/design-docs/tablet.md existed for apache doris.

FOr inference, less than 1KLOC of pure, dependency-free C is enough (if you include the tokenizer and command line parsing)[1]. This was a non-obvious fact for me, in principle, you could run a modern LLM 20 years ago with just 1000 lines of code, assuming you're fine with things potentially taking days to run of course.

Training wouldn't be that much harder, Micrograd[2] is 200LOC of pure Python, 1000 lines would probably be enough for training an (extremely slow) LLM. By "extremely slow", I mean that a training run that normally takes hours could probably take dozens of years, but the results would, in principle, be the same.

If you were writing in C instead of Python and used something like Llama CPP's optimization tricks, you could probably get somewhat acceptable training performance in 2 or 3 KLOC. You'd still be off by one or two orders of magnitude when compared to a GPU cluster, but a lot better than naive, loopy Python.

[1] https://github.com/karpathy/llama2.c

[2] https://github.com/karpathy/micrograd

A bit off topic but ML-guru Mr Karpathy has implemented a state-of-art Llama2 model in a plain C with no dependencies on 3rd party/freeware libraries. See repo.

Related. I built karpathy’s llama2.c (https://github.com/karpathy/llama2.c) without modifications to WASM and run it in the browser. It was a fun exercise to directly compare native vs. Web perf. Getting 80% of native performance on my M1 Macbook Air and haven’t spent anytime optimizing the WASM side.

Demo: https://diegomarcos.com/llama2.c-web/

Code:

Would be cool for there to be a `llama2.f`, similar to https://github.com/karpathy/llama2.c, to demo it's capabilities

This is a fork of https://github.com/karpathy/llama2.c

karpathy's llama2.c is like llama.cpp but it is written in C and the python training code is available in that same repo. llama2.c's goal is to be a elegant single file C implementation of the inference and an elegant python implementation for training.

His goal is for people to understand how llama 2 and LLM's work, so he keeps it simple and sweet. As the project progresses, so will features and performance improvements added.

Currently it can infer baby (small) Story models trained by Karpathy at a fast pace. It can also infer Meta LLAMA 2 7b models, but at a very slow rate such as 1 token per second.

So currently this can be used for learning or as a tech preview.

Our friendly fork tries to make it portable, performant and more usable (bells and whistles) over time. Since we mirror upstream closely, the inference capabilities of our fork is similar but slightly faster if compiled with acceleration. What we try to do different is that we try to make this bootable (not there yet) and portable. Right now you can get binary portablity - use the same run.com on any x86_64 machine running on any OS, it will work (possible due to cosmopolitan toolchain). The other part that works is unikernels - boot this as unikernel in VM's (possible due unikraft unikernel & toolchain).

See our fork currently as a release early and release often toy tech demo. We plan to build it out into a useful product.

In my latest release of ggml.js, I've added support for Karapathy's llama2.c model.

I really enjoyed Anrej Kaparthy's llama2.c project (https://github.com/karpathy/llama2.c), which runs through creating and running a miniature Llama2 architecture model from scratch.

- https://github.com/karpathy/llama2.c

I think there may be some applications in this limited space that are worth looking into. You won’t replicate GPT-anything but it may be possible to solve some nice problems very much more efficiently that one would expect at first.

It does now: https://github.com/karpathy/llama2.c#metas-llama-2-models