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7 Commits

Author SHA1 Message Date
josc146
1b83bf261a release v1.6.5 2023-12-14 22:07:17 +08:00
josc146
2a7d22dab1 Composition Option: Only Auto Play Generated Content 2023-12-14 22:06:39 +08:00
josc146
f7494b0cfb update midi_filter_config.json 2023-12-14 21:18:48 +08:00
github-actions[bot]
9ca91d59ec release v1.6.4 2023-12-14 12:40:56 +00:00
josc146
11feaa6e68 release v1.6.4 2023-12-14 20:40:24 +08:00
josc146
18d4b2304e WebGPU (Python) strategy 2023-12-14 20:39:42 +08:00
github-actions[bot]
2f45e9c33a release v1.6.3 2023-12-14 10:43:36 +00:00
10 changed files with 44 additions and 17 deletions

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@@ -1,9 +1,7 @@
## Changes
- add WebGPU Python Mode (https://github.com/cryscan/web-rwkv-py)
- bump MIDI-LLM-tokenizer (fix note off)
- fix refreshBuiltInModels
- chore
- update midi_filter_config.json
- Composition Option: Only Auto Play Generated Content
## Install

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@@ -12,8 +12,13 @@ except ModuleNotFoundError:
class RWKV:
def __init__(self, model_path: str, strategy=None):
self.model = wrp.v5.Model(model_path, turbo=False)
def __init__(self, model_path: str, strategy: str = None):
self.model = wrp.v5.Model(
model_path,
turbo=False,
quant=32 if "i8" in strategy else None,
quant_nf4=26 if "i4" in strategy else None,
)
self.w = {} # fake weight
self.w["emb.weight"] = [0] * wrp.peek_info(model_path).num_vocab

View File

@@ -1,5 +1,5 @@
{
"deduplicate_md5": true,
"piece_split_delay": 10.0,
"min_piece_length": 30.0
"piece_split_delay": 10000,
"min_piece_length": 0
}

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@@ -320,5 +320,6 @@
"Play With External Player": "外部プレーヤーで再生",
"Core API URL": "コアAPI URL",
"Override core API URL(/chat/completions and /completions). If you don't know what this is, leave it blank.": "コアAPI URLを上書きします(/chat/completions と /completions)。何であるかわからない場合は空白のままにしてください。",
"Please change Strategy to CPU (rwkv.cpp) to use ggml format": "StrategyをCPU (rwkv.cpp)に変更して、ggml形式を使用してください"
"Please change Strategy to CPU (rwkv.cpp) to use ggml format": "StrategyをCPU (rwkv.cpp)に変更して、ggml形式を使用してください",
"Only Auto Play Generated Content": "生成されたコンテンツのみ自動再生"
}

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@@ -320,5 +320,6 @@
"Play With External Player": "使用外部播放器播放",
"Core API URL": "核心 API URL",
"Override core API URL(/chat/completions and /completions). If you don't know what this is, leave it blank.": "覆盖核心的 API URL (/chat/completions 和 /completions)。如果你不知道这是什么,请留空",
"Please change Strategy to CPU (rwkv.cpp) to use ggml format": "请将Strategy改为CPU (rwkv.cpp)以使用ggml格式"
"Please change Strategy to CPU (rwkv.cpp) to use ggml format": "请将Strategy改为CPU (rwkv.cpp)以使用ggml格式",
"Only Auto Play Generated Content": "仅自动播放新生成的内容"
}

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@@ -152,10 +152,14 @@ const CompositionPanel: FC = observer(() => {
if (autoPlay) {
if (commonStore.compositionParams.externalPlay)
externalPlayListener();
else
else {
if (commonStore.compositionParams.playOnlyGeneratedContent && playerRef.current) {
playerRef.current.currentTime = Math.max(commonStore.compositionParams.generationStartTime - 1, 0);
}
setTimeout(() => {
playerRef.current?.start();
});
}
}
});
});
@@ -314,6 +318,14 @@ const CompositionPanel: FC = observer(() => {
autoPlay: data.checked as boolean
});
}} />
<Checkbox className="select-none"
size="large" label={t('Only Auto Play Generated Content')} checked={params.playOnlyGeneratedContent}
onChange={async (_, data) => {
setParams({
autoPlay: data.checked as boolean || commonStore.compositionParams.autoPlay,
playOnlyGeneratedContent: data.checked as boolean
});
}} />
<Labeled flex breakline label={t('MIDI Input')}
desc={t('Select the MIDI input device to be used.')}
content={
@@ -359,6 +371,9 @@ const CompositionPanel: FC = observer(() => {
contentText={t('Are you sure you want to reset this page? It cannot be undone.')}
onConfirm={() => {
commonStore.setCompositionSubmittedPrompt(defaultCompositionPrompt);
setParams({
generationStartTime: 0
});
setPrompt(defaultCompositionPrompt);
}} />
<Button className="grow" appearance="primary" onClick={() => {
@@ -368,6 +383,9 @@ const CompositionPanel: FC = observer(() => {
generateNs(params.autoPlay);
} else {
commonStore.setCompositionGenerating(true);
setParams({
generationStartTime: playerRef.current ? playerRef.current.duration : 0
});
onSubmit(params.prompt);
}
}}>{!commonStore.compositionGenerating ? t('Generate') : t('Stop')}</Button>

View File

@@ -282,7 +282,7 @@ const Configs: FC = observer(() => {
selectedConfig.modelParameters.device !== 'Custom' && <Labeled label={t('Precision')}
desc={t('int8 uses less VRAM, but has slightly lower quality. fp16 has higher quality.')}
content={
<Dropdown disabled={selectedConfig.modelParameters.device === 'WebGPU (Python)'}
<Dropdown
style={{ minWidth: 0 }} className="grow"
value={selectedConfig.modelParameters.precision}
selectedOptions={[selectedConfig.modelParameters.precision]}
@@ -296,8 +296,8 @@ const Configs: FC = observer(() => {
{selectedConfig.modelParameters.device !== 'CPU' && selectedConfig.modelParameters.device !== 'MPS' &&
<Option>fp16</Option>}
{selectedConfig.modelParameters.device !== 'CPU (rwkv.cpp)' && <Option>int8</Option>}
{selectedConfig.modelParameters.device === 'WebGPU' && <Option>nf4</Option>}
{selectedConfig.modelParameters.device !== 'CPU (rwkv.cpp)' && selectedConfig.modelParameters.device !== 'WebGPU' &&
{selectedConfig.modelParameters.device.startsWith('WebGPU') && <Option>nf4</Option>}
{selectedConfig.modelParameters.device !== 'CPU (rwkv.cpp)' && !selectedConfig.modelParameters.device.startsWith('WebGPU') &&
<Option>fp32</Option>}
{selectedConfig.modelParameters.device === 'CPU (rwkv.cpp)' && <Option>Q5_1</Option>}
</Dropdown>

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@@ -96,7 +96,9 @@ class CommonStore {
useLocalSoundFont: false,
externalPlay: false,
midi: null,
ns: null
ns: null,
generationStartTime: 0,
playOnlyGeneratedContent: true
};
compositionGenerating: boolean = false;
compositionSubmittedPrompt: string = defaultCompositionPrompt;

View File

@@ -11,7 +11,9 @@ export type CompositionParams = {
useLocalSoundFont: boolean,
externalPlay: boolean,
midi: ArrayBuffer | null,
ns: NoteSequence | null
ns: NoteSequence | null,
generationStartTime: number,
playOnlyGeneratedContent: boolean,
}
export type Track = {
id: string;

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@@ -1,5 +1,5 @@
{
"version": "1.6.2",
"version": "1.6.4",
"introduction": {
"en": "RWKV is an open-source, commercially usable large language model with high flexibility and great potential for development.\n### About This Tool\nThis tool aims to lower the barrier of entry for using large language models, making it accessible to everyone. It provides fully automated dependency and model management. You simply need to click and run, following the instructions, to deploy a local large language model. The tool itself is very compact and only requires a single executable file for one-click deployment.\nAdditionally, this tool offers an interface that is fully compatible with the OpenAI API. This means you can use any ChatGPT client as a client for RWKV, enabling capability expansion beyond just chat functionality.\n### Preset Configuration Rules at the Bottom\nThis tool comes with a series of preset configurations to reduce complexity. The naming rules for each configuration represent the following in order: device - required VRAM/memory - model size - model language.\nFor example, \"GPU-8G-3B-EN\" indicates that this configuration is for a graphics card with 8GB of VRAM, a model size of 3 billion parameters, and it uses an English language model.\nLarger model sizes have higher performance and VRAM requirements. Among configurations with the same model size, those with higher VRAM usage will have faster runtime.\nFor example, if you have 12GB of VRAM but running the \"GPU-12G-7B-EN\" configuration is slow, you can downgrade to \"GPU-8G-3B-EN\" for a significant speed improvement.\n### About RWKV\nRWKV is an RNN with Transformer-level LLM performance, which can also be directly trained like a GPT transformer (parallelizable). And it's 100% attention-free. You only need the hidden state at position t to compute the state at position t+1. You can use the \"GPT\" mode to quickly compute the hidden state for the \"RNN\" mode.<br/>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 (using the final hidden state).",
"zh": "RWKV是一个开源且允许商用的大语言模型灵活性很高且极具发展潜力。\n### 关于本工具\n本工具旨在降低大语言模型的使用门槛做到人人可用本工具提供了全自动化的依赖和模型管理你只需要直接点击运行跟随引导即可完成本地大语言模型的部署工具本身体积极小只需要一个exe即可完成一键部署。\n此外本工具提供了与OpenAI API完全兼容的接口这意味着你可以把任意ChatGPT客户端用作RWKV的客户端实现能力拓展而不局限于聊天。\n### 底部的预设配置规则\n本工具内置了一系列预设配置以降低使用难度每个配置名的规则依次代表着设备-所需显存/内存-模型规模-模型语言。\n例如GPU-8G-3B-CN表示该配置用于显卡需要8G显存模型规模为30亿参数使用的是中文模型。\n模型规模越大性能要求越高显存要求也越高而同样模型规模的配置中显存占用越高的运行速度越快。\n例如当你有12G显存但运行GPU-12G-7B-CN配置速度比较慢可降级成GPU-8G-3B-CN将会大幅提速。\n### 关于RWKV\nRWKV是具有Transformer级别LLM性能的RNN也可以像GPT Transformer一样直接进行训练可并行化。而且它是100% attention-free的。你只需在位置t处获得隐藏状态即可计算位置t + 1处的状态。你可以使用“GPT”模式快速计算用于“RNN”模式的隐藏状态。\n因此它将RNN和Transformer的优点结合起来 - 高性能、快速推理、节省显存、快速训练、“无限”上下文长度以及免费的语句嵌入(使用最终隐藏状态)。"