RWKV-infctx-trainer
RWKV infctx trainer, for training arbitary context sizes, to 10k and beyond! (by RWKV)
faster-rwkv
By daquexian
| RWKV-infctx-trainer | faster-rwkv | |
|---|---|---|
| 1 | 1 | |
| 125 | 121 | |
| 2.4% | - | |
| 9.6 | 9.1 | |
| 28 days ago | 6 months ago | |
| Jupyter Notebook | C++ | |
| 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.
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.
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.
What are some alternatives?
When comparing RWKV-infctx-trainer and faster-rwkv 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.