Commit
·
ce40565
1
Parent(s):
a97f966
Update README.md
Browse files
README.md
CHANGED
|
@@ -7,4 +7,34 @@ sdk: static
|
|
| 7 |
pinned: false
|
| 8 |
---
|
| 9 |
|
| 10 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 7 |
pinned: false
|
| 8 |
---
|
| 9 |
|
| 10 |
+
# Introducing Lamini, the LLM Engine for Rapid Customization
|
| 11 |
+
|
| 12 |
+
[Lamini](lamini.ai) gives every developer the superpowers that took the world from GPT-3 to ChatGPT!
|
| 13 |
+
|
| 14 |
+
Today, you can try out our open dataset generator for training instruction-following LLMs (like ChatGPT) on [Github](https://lamini.ai/).
|
| 15 |
+
|
| 16 |
+
[Sign up](https://lamini.ai/contact) for early access to our full LLM training module, including enterprise features like cloud prem deployments.
|
| 17 |
+
|
| 18 |
+
|
| 19 |
+
# Training LLMs should be as easy as prompt-tuning 🦾
|
| 20 |
+
Why is writing a prompt so easy, but training an LLM from a base model still so hard? Iteration cycles for finetuning on modest datasets are measured in months because it takes significant time to figure out why finetuned models fail. Conversely, prompt-tuning iterations are on the order of seconds, but performance plateaus in a matter of hours. Only a limited amount of data can be crammed into the prompt, not the terabytes of data in a warehouse.
|
| 21 |
+
|
| 22 |
+
It took OpenAI months with an incredible ML team to fine-tune and run RLHF on their base GPT-3 model that was available for years — creating what became ChatGPT. This training process is only accessible to large ML teams, often with PhDs in AI.
|
| 23 |
+
|
| 24 |
+
Technical leaders at Fortune 500 companies have told us:
|
| 25 |
+
|
| 26 |
+
* “Our team of 10 machine learning engineers hit the OpenAI finetuning API, but our model got worse — help!”
|
| 27 |
+
* “I don’t know how to make the best use of my data — I’ve exhausted all the prompt magic we can summon from tutorials online.”
|
| 28 |
+
That’s why we’re building Lamini: to give every developer the superpowers that took the world from GPT-3 to ChatGPT.
|
| 29 |
+
|
| 30 |
+
|
| 31 |
+
# Rapidly train LLMs to be as good as ChatGPT from any base model 🚀
|
| 32 |
+
Lamini is an LLM engine that allows any developer, not just machine learning experts, to train high-performing LLMs on large datasets using the Lamini library.
|
| 33 |
+
|
| 34 |
+
The optimizations in this library reach far beyond what’s available to developers now, from more challenging ones like RLHF to simpler ones like reducing hallucinations.
|
| 35 |
+
|
| 36 |
+

|
| 37 |
+
|
| 38 |
+
Lamini runs across platforms, from OpenAI’s models to open-source ones on HuggingFace, with more to come soon. We are agnostic to base models, as long as there’s a way for our engine to train and run them. In fact, Lamini makes it easy to run multiple base model comparisons in just a single line of code.
|
| 39 |
+
|
| 40 |
+
Now that you know a bit about where we’re going, today, we’re excited to release our first major community resource!
|