TinyLlama
nessie
TinyLlama | nessie | |
---|---|---|
14 | 13 | |
6,818 | 834 | |
- | 3.6% | |
8.7 | 9.9 | |
18 days ago | 5 days ago | |
Python | Java | |
Apache License 2.0 | Apache License 2.0 |
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TinyLlama
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What are LLMs? An intro into AI, models, tokens, parameters, weights, quantization and more
Small models: Less than ~1B parameters. TinyLlama and tinydolphin are examples of small models.
- FLaNK Stack Weekly 22 January 2024
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TinyLlama: An Open-Source Small Language Model
GitHub repo with links to the checkpoints: https://github.com/jzhang38/TinyLlama
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NLP Research in the Era of LLMs
> While LLM projects typically require an exorbitant amount of resources, it is important to remind ourselves that research does not need to assemble full-fledged massively expensive systems in order to have impact.
Check out TinyLlama; https://github.com/jzhang38/TinyLlama
Four research students from Singapore University of Technology and Design are pretraining a 1.1B Llama model on 3 trillion token using a handful of A100's.
They're also providing the source code, training data, and fine-tuned checkpoints for anyone to run.
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TinyLlama - Any news?
The first one was that the minimum learning rate was mistakenly set to the same value as the maximum learning rate in cosine decay, so the learning rate wasn't decreasing. This was discovered relatively early during training and discussed in this issue: https://github.com/jzhang38/TinyLlama/issues/27
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Llamafile lets you distribute and run LLMs with a single file
Which is a smaller model, that gives good output and that works best with this. I am looking to run this on lower end systems.
I wonder if someone has already tried https://github.com/jzhang38/TinyLlama, could save me some time :)
- FLaNK Stack Weekly for 20 Nov 2023
- New 1.5T token checkpoint of TinyLLaMa got released!
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What Every Developer Should Know About GPU Computing
I thought I'd share something with my experience with HPC that applies to many areas, especially in the rise of GPUs.
The main bottleneck isn't compute, it is memory. If you go to talks you're gonna see lots of figures like this one[0] (typically also showing disk speeds, which are crazy small).
Compute is increasing so fast that at this point we finish our operations long faster than it takes to save those simulations or even create the visualizations and put on disk. There's a lot of research going into this, with a lot of things like in situ computing (asynchronous operations, often pushing to a different machine, but needing many things like flash buffers. See ADIOS[1] as an example software).
What I'm getting at here is that we're at a point where we have to think about that IO bottleneck, even for non-high performance systems. I work in ML now, which we typically think of as compute bound, but being in the generative space there are still many things where the IO bottlenecks. This can be loading batches into memory, writing results to disk, or communication between distributed processes. It's one beg reason we typically want to maximize memory usage (large batches).
There's a lot of low hanging fruit in these areas that aren't going to be generally publishable works but are going to have lots of high impact. Just look at things like LLaMA CPP[2], where in the process they've really decreased the compute time and memory load. There's also projects like TinyLLaMa[3] who are exploring training a 1B model and doing so on limited compute, and are getting pretty good results. But I'll tell you from personal experience, small models and limited compute experience doesn't make for good papers (my most cited work did this and has never been published, gotten many rejections for not competing with models 100x it's size, but is also quite popular in the general scientific community who work with limited compute). Wfiw, companies that are working on applications do value these things, but it is also noise in the community that's hard to parse. Idk how we can do better as a community to not get trapped in these hype cycles, because real engineering has a lot of these aspects too, and they should be (but aren't) really good areas for academics to be working in. Scale isn't everything in research, and there's a lot of different problems out there that are extremely important but many are blind to.
And one final comment, there's lots of code that is used over and over that are not remotely optimized and can be >100x faster. Just gotta slow down and write good code. The move fast and break things method is great for getting moving but the debt compounds. It's just debt is less visible, but there's so much money being wasted from writing bad code (and LLMs are only going to amplify this. They were trained on bad code after all)
[0] https://drivenets.com/wp-content/uploads/2023/05/blog-networ...
[1] https://github.com/ornladios/ADIOS2
[2] https://github.com/ggerganov/llama.cpp
[3] https://github.com/jzhang38/TinyLlama
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Mistral 7B Paper on ArXiv
As discussed in the original GPT3 paper (https://twitter.com/gneubig/status/1286731711150280705?s=20)
TinyLlama is trying to do that for 1.1B: https://github.com/jzhang38/TinyLlama
As long as we are not at the capacity limit, we will have a few of these 7B beats 13B (or 7B beats 70B) moments.
nessie
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A deep dive into the concept and world of Apache Iceberg Catalogs
Nessie is an innovative open-source catalog that extends beyond the traditional catalog capabilities in the Apache Iceberg ecosystem, introducing git-like features to data management. This catalog not only tracks table metadata but also allows users to capture commits at a holistic level, enabling advanced operations such as multi-table transactions, rollbacks, branching, and tagging. These features provide a new layer of flexibility and control over data changes, resembling version control systems in software development.
- FLaNK Stack Weekly 22 January 2024
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Why is Hive Metastore everywhere? (Especially Iceberg)
Try Nessie https://github.com/projectnessie/nessie - it recently got trino support as well ..
- What are the main things I need to know to be hired as a Java developer?
- Is learning and mastering Spring & Spring boot worth it in 2023 ?
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Which lakehouse table format do you expect your organization will be using by the end of 2023?
Project Nessie (https://projectnessie.org/) will be the catalog that eventually decouples Iceberg from Hive. At that point, I think it will be a no brainer to go Iceberg over Delta.
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5 Reasons Your Data Lakehouse should Embrace Dremio Cloud
The Dremio Sonar query engine can query your data where it exists whether it's AWS Glue, S3, Nessie Catalogs, MySQL, Postgres, RedShift and an ever growing list of sources.
- Project Nessie: Transactional Catalog for Data Lakes with Git-Like Semantics
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Introduction to The World of Data - (OLTP, OLAP, Data Warehouses, Data Lakes and more)
We will also need a catalog to track all of these tables, with the open source Project Nessie we can do just that, and also get great versioning features similar to using Git when developing applications allowing data engineers to practice "data as code" and "write-audit-publish" patterns on their data.
- DoltLab v0.2.0
What are some alternatives?
langchain - 🦜🔗 Build context-aware reasoning applications
git-bug - Distributed, offline-first bug tracker embedded in git, with bridges
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
dvc - 🦉 ML Experiments and Data Management with Git
public - A collection of my cources, lectures, articles and presentations
hiveberg - Demonstration of a Hive Input Format for Iceberg
llamafile - Distribute and run LLMs with a single file.
dremio-oss - Dremio - the missing link in modern data
ADIOS2 - Next generation of ADIOS developed in the Exascale Computing Program
noms - The versioned, forkable, syncable database
airoboros - Customizable implementation of the self-instruct paper.
dolt - Dolt – Git for Data