Nicolay Gerold
@nicolaygerold
Nicolay Gerold
On the one hand, quality data still matters. A lot of focus on LLM improvement is on model and dataset size. There’s some early evidence that LLMs can be greatly influenced by the data quality they are trained with. WizardLM, TinyStories, and phi-1 are some examples. Likewise, RLHF datasets also matter.
On the other hand, ~100 data points is enough... See more
On the other hand, ~100 data points is enough... See more
Is Data Still a Moat?
Inspiration
Watch the explainer video
Open your ~/Downloads directory. Or your Desktop. It's probably a mess...
What it does
LlamaFS is a self-organizing file manager. It automatically renames and organizes your files based on their contents and well-known... See more
Watch the explainer video
Open your ~/Downloads directory. Or your Desktop. It's probably a mess...
There are only two hard things in Computer Science: cache invalidation and naming things .
What it does
LlamaFS is a self-organizing file manager. It automatically renames and organizes your files based on their contents and well-known... See more
GitHub - iyaja/llama-fs: A self-organizing file system with llama 3
Cube is an open source semantic layer which recently integrated with LangChain to solve these issues in a different way. You can use YAML, Javascript, or Python to create definitions of different metrics, measures and dimensions for your data:
Artyom Keydunov • Powering your Copilot for Data – with Artem Keydunov of Cube.dev
(1) Tasks (a) Data collection, cleaning & labeling: human annotators , exploratory data analysis (b) Embeddings & feature engineering: normalization , bucketing / binning , word2vec (c) Data modeling & experimentation: accuracy , F1-score , precision , recall (d) Testing: scenario testing , AB testing , adaptive test-data (2) Biz/Org Management (a)... See more
Shreya Shankar • "We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learning.
⚡ LitGPT
Pretrain, finetune, evaluate, and deploy 20+ LLMs on your own data
Uses the latest state-of-the-art techniques:
✅ flash attention ✅ fp4/8/16/32 ✅ LoRA, QLoRA, Adapter (v1, v2) ✅ FSDP ✅ 1-1000+ GPUs/TPUs
Lightning AI • Models • Quick start • Inference • Finetune • Pretrain • Deploy • Features • Training recipes (YAML)
Finetune, pretrain and... See more
Pretrain, finetune, evaluate, and deploy 20+ LLMs on your own data
Uses the latest state-of-the-art techniques:
✅ flash attention ✅ fp4/8/16/32 ✅ LoRA, QLoRA, Adapter (v1, v2) ✅ FSDP ✅ 1-1000+ GPUs/TPUs
Lightning AI • Models • Quick start • Inference • Finetune • Pretrain • Deploy • Features • Training recipes (YAML)
Finetune, pretrain and... See more
Lightning-AI • GitHub - Lightning-AI/litgpt: Pretrain, finetune, deploy 20+ LLMs on your own data. Uses state-of-the-art techniques: flash attention, FSDP, 4-bit, LoRA, and more.
Requesting a large amount of generated tokens completions can lead to increased latencies:
- Lower max tokens : for requests with a similar token generation count, those that have a lower max_tokens parameter incur less latency.
- Include stop sequences : to prevent generating unneeded tokens, add a stop sequence. For example, you can use stop
OpenAI Platform
Model Explorer is a powerful graph visualization tool that helps one understand, debug, and optimize ML models. It specializes in visualizing large graphs in an intuitive, hierarchical format, but works well for smaller models as well.
Graph visualization plays a pivotal role in the machine learning (ML) development process. Visual representations... See more
Graph visualization plays a pivotal role in the machine learning (ML) development process. Visual representations... See more
Model Explorer: Graph visualization for large model development
You can think your way into solving a deterministic system, but you cannot think your way into solving a probabilistic system.
The first thing that I want to call out is that deterministic software has edge cases, while probabilistic software has long tails.
I find that a lot of junior folks try to really think hard about edge cases around... See more
Jason Liu • Tips for probabilistic software - jxnl.co
Build & Deployments
Our build process starts by pushing changes to a repository on GitHub. When code is pushed to a repository through a pull request, it triggers a job to build the changes made to the branch and deploy them in isolation. This worklow happens interactively within the pull request, where the author has visibility of all steps.
Some... See more
Our build process starts by pushing changes to a repository on GitHub. When code is pushed to a repository through a pull request, it triggers a job to build the changes made to the branch and deploy them in isolation. This worklow happens interactively within the pull request, where the author has visibility of all steps.
Some... See more