---
language:
- en
tags:
- ggml
- text-generation
- causal-lm
- rwkv
license: apache-2.0
datasets:
- EleutherAI/pile
- togethercomputer/RedPajama-Data-1T
---

**Last updated:** 2023-06-07

This is [BlinkDL/rwkv-4-pileplus](https://huggingface.co/BlinkDL/rwkv-4-pileplus) converted to GGML for use with rwkv.cpp and KoboldCpp. [rwkv.cpp's conversion instructions](https://github.com/saharNooby/rwkv.cpp#option-32-convert-and-quantize-pytorch-model) were followed.

**NOTE:** If you're like me and you want to run this model on a 32-bit ARM processor, keep in mind that KoboldCpp/llama.cpp and similar projects don't yet have support for 32-bit ARM as of 2023-07-22. You'll need to compile a 64-bit ARM binary (easiest done through a 64-bit ARM system) and then run it through [QEMU user space emulation](https://www.qemu.org/docs/master/user/main.html) (slow) or [QEMU full system emulation](https://wiki.debian.org/QEMU#Setting_up_a_testing.2Funstable_system) (slower).

Running a 3B model on an emulated x86-64 (on my PC, nonetheless) gave me a speed that felt like a single token every 30 seconds, so the payoff may not be worth it until official support is implemented.

### RAM USAGE (KoboldCpp)
Model | RAM usage (with OpenBLAS)
:--:|:--:
Unloaded | 41.3 MiB
169M q4_0 | 232.2 MiB
169M q5_0 | 243.3 MiB
169M q5_1 | 249.2 MiB
430M q4_0 | 413.2 MiB
430M q5_0 | 454.4 MiB
430M q5_1 | 471.8 MiB
1.5B q4_0 | 1.1 GiB
1.5B q5_0 | 1.3 GiB
1.5B q5_1 | 1.3 GiB
3B q4_0 | 2.0 GiB
3B q5_0 | 2.3 GiB
3B q5_1 | 2.4 GiB

Original model card by BlinkDL is below.

* * *

# RWKV-4 PilePlus

## Model Description

RWKV-4-pile models finetuning on [RedPajama + some of Pile v2 = 1.7T tokens]. Updated with 2020+2021+2022 data, and better at all European languages.

Although some of these are intermedia checkpoints (XXXGtokens means finetuned for XXXG tokens), you can already use them because I am finetuning from Pile models (instead of retraining).

Note: not instruct tuned yet, and recommended to replace vanilla Pile models.

7B and 14B coming soon.

See https://github.com/BlinkDL/RWKV-LM for details.

Use https://github.com/BlinkDL/ChatRWKV to run it.