faster-rwkv
By daquexian
RWKV-infctx-trainer
RWKV infctx trainer, for training arbitary context sizes, to 10k and beyond! (by RWKV)
| faster-rwkv | RWKV-infctx-trainer | |
|---|---|---|
| 1 | 1 | |
| 122 | 123 | |
| - | 6.5% | |
| 9.1 | 9.7 | |
| 6 months ago | 15 days ago | |
| C++ | Jupyter Notebook | |
| - | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
faster-rwkv
Posts with mentions or reviews of faster-rwkv.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-09.
RWKV-infctx-trainer
Posts with mentions or reviews of RWKV-infctx-trainer.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-09.
What are some alternatives?
When comparing faster-rwkv and RWKV-infctx-trainer you can also consider the following projects:
RWKV-LM-LoRA - RWKV is a RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
RWKV-LM - RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.