RWKV
The RWKV Language Model
Training RWKV (and latest developments) RWKV App for Android / iOS / PC / Mac / Linux Very efficient inference (7B fp16 bsz960 = 10250+ tps on 5090) RWKV pip reference (slower) package Finetuning RWKV (9GB VRAM can finetune 7B) WebGPU inference (NVIDIA/AMD/Intel), nf4/int8/fp16 with history of RWKV from v1 to v7 (note: AI-written)
StarCoder
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The model was trained on GitHub code as well as additional selected data sources such as Arxiv and Wikipedia. As such it is not an instruction model and commands like "Write a function that computes the square root." do not work well. Here are some examples to get started with the model. You can find a script for fine-tuning in StarCoder2's GitHub repository. First, make sure to install transformers from source: The pretraining dataset of the model was filtered for permissive licenses and code with no license only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a search index that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code. The model has been trained on source code from 600+ programming languages. The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. See the paper for an in-depth discussion of the model limitations. The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement here.
RWKV
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RWKV
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RWKV
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RWKV
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