txtinstruct
lm-evaluation-harness
| txtinstruct | lm-evaluation-harness | |
|---|---|---|
| 13 | 1 | |
| 221 | 91 | |
| 2.7% | - | |
| 5.0 | 3.7 | |
| 10 months ago | about 1 year ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
txtinstruct
-
Questions about memory, tree-of-thought, planning
I tried cromadb but had terrible performance and could not pin down the cause (likely a problem on my end). Weaviate was easy to setup and had excellent performance, this is probably what I will use in the future. Next on my list is txtinstruct, to finetune a model with data that does not change and using a vector db for everything else seems promising.
-
[R] Let Language Models be Language Models
The closest thing I've seen to this is txtinstruct
-
Create a ChatGPT-like program using an open source model and custom data.
txtinstruct is a framework for training instruction-tuned models
-
Stability AI Launches the First of Its StableLM Suite of Language Models
Great to see the continued release of open models. The only disappointing thing is that models keep building on CC-BY-NC licensed datasets, which severely limits their use.
Hopefully, people consider txtinstruct (https://github.com/neuml/txtinstruct) and other approaches to generate instruction-tuning datasets without the baggage.
- Build open instruction-tuned datasets and models (r/MachineLearning)
- Build open instruction-tuned datasets and models
- [P] Build open instruction-tuned datasets and models
- Create open instruction-tuned datasets and LLM models
- Show HN: Build open instruction-tuned datasets and models
lm-evaluation-harness
-
Stability AI Launches the First of Its StableLM Suite of Language Models
Yeah, although looks like it currently has some issues with coqa: https://github.com/EleutherAI/lm-evaluation-harness/issues/2...
There's also the bigscience fork, but I ran into even more problems (although I didn't try too hard) https://github.com/bigscience-workshop/lm-evaluation-harness
And there's https://github.com/EleutherAI/lm-eval2/ (not sure if it's just starting over w/ a new repo or what?) but it has limited tests available
What are some alternatives?
StableLM - StableLM: Stability AI Language Models
instruct-eval - This repository contains code to quantitatively evaluate instruction-tuned models such as Alpaca and Flan-T5 on held-out tasks.
safetensors - Simple, safe way to store and distribute tensors
AlpacaDataCleaned - Alpaca dataset from Stanford, cleaned and curated
lm-eval2
geov - The GeoV model is a large langauge model designed by Georges Harik and uses Rotary Positional Embeddings with Relative distances (RoPER). We have shared a pre-trained 9B parameter model.
flash-attention - Fast and memory-efficient exact attention
cataclysm - Cataclysm - Code generation library for the end game
lm-evaluation-harness - A framework for few-shot evaluation of language models.