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13 Commits

Author SHA1 Message Date
josc146
d075d6377e release v1.7.9 2024-04-30 23:05:51 +08:00
josc146
ae1d01bd0c update manifest.json 2024-04-30 22:39:06 +08:00
josc146
aae7cfe1a2 change the default value of presystem to false 2024-04-30 22:30:06 +08:00
josc146
38b33a7030 upgrade to rwkv 0.8.26 (state instruct align support) 2024-04-30 22:24:22 +08:00
josc146
70236df3d1 update defaultModelConfigs 2024-04-30 21:58:16 +08:00
josc146
40c5368deb chore 2024-04-30 21:55:24 +08:00
josc146
2d853f92b9 small fix 2024-04-30 21:52:47 +08:00
josc146
2a0ad19bc5 update manifest.json 2024-04-19 13:07:12 +08:00
github-actions[bot]
5deb115625 release v1.7.8 2024-04-03 06:56:57 +00:00
josc146
7f329702ad release v1.7.8 2024-04-03 14:56:07 +08:00
josc146
ff6240d798 chore 2024-04-03 14:54:02 +08:00
josc146
f6614ff4dc update manifest.json 2024-03-30 13:57:54 +08:00
github-actions[bot]
8633134de7 release v1.7.7 2024-03-27 02:20:53 +00:00
12 changed files with 181 additions and 66 deletions

View File

@@ -1,7 +1,41 @@
## Changes
- avoid program lag caused by frequent triggering of read/write operations due to Linux file system notification
- improve styles
- bump webgpu mode [ai00_server v0.4.2](https://github.com/Ai00-X/ai00_server) (huge performance improvement)
- upgrade to rwkv 0.8.26 (state-tuned model support)
- update defaultConfigs and manifest.json
- chores
## Breaking Changes
- change the default value of `presystem` to false
For the convenience of using the future state-tuned models, the default value of `presystem` has been set to false. This
means that the RWKV-Runner service will no longer automatically insert recommended RWKV pre-prompts for you:
```
User: hi
Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.
```
If you are using the API service and conducting a rigorous RWKV conversation, please manually send the above messages to
the `/chat/completions` API's `messages` array, or manually send `presystem: true` to have the server automatically
insert pre-prompts.
If you are using the RWKV-Runner client for chatting, you can enable `Insert default system prompt at the beginning` in
the preset editor.
Of course, in reality, even if you do not perform the above, there is usually no significant negative impact.
If you are using the new RWKV state-tuned models, you do not need to perform the above.
The new RWKV state-tuned models can be downloaded here, they are very interesting:
- https://huggingface.co/BlinkDL/rwkv-6-state-instruct-aligned
- https://huggingface.co/BlinkDL/temp-latest-training-models
If you are interested in state-tuning, please refer
to: https://github.com/BlinkDL/RWKV-LM#state-tuning-tuning-the-initial-state-zero-inference-overhead
## Install

View File

@@ -102,6 +102,7 @@ if __name__ == "__main__":
"time_mix_w2",
"time_decay_w1",
"time_decay_w2",
"lora.0",
],
)
print(f"Saved to {args.output}")

View File

@@ -1,7 +1,7 @@
torch
torchvision
torchaudio
rwkv==0.8.25
rwkv==0.8.26
langchain==0.0.322
fastapi==0.109.1
uvicorn==0.23.2

View File

@@ -1,7 +1,7 @@
torch
torchvision
torchaudio
rwkv==0.8.25
rwkv==0.8.26
langchain==0.0.322
fastapi==0.109.1
uvicorn==0.23.2

View File

@@ -57,7 +57,7 @@ class ChatCompletionBody(ModelConfigBody):
None, description="Internal system name", min_length=1
)
presystem: bool = Field(
True, description="Whether to insert default system prompt at the beginning"
False, description="Whether to insert default system prompt at the beginning"
)
model_config = {
@@ -382,7 +382,7 @@ async def chat_completions(body: ChatCompletionBody, request: Request):
body.stop.append(f"\n\n{user_code}")
body.stop.append(f"\n\n{bot_code}")
elif body.stop is None:
body.stop = default_stop
body.stop = default_stop + [f"\n\n{user_code}", f"\n\n{bot_code}"]
# if not body.presystem:
# body.stop.append("\n\n")

View File

@@ -488,14 +488,19 @@ class RWKV(MyModule):
print_need_newline = False
REAL_TIME_FIRST = False
args.time_state = False
for x in list(w.keys()):
if ".time_faaaa" in x:
REAL_TIME_FIRST = True
if ".time_state" in x:
args.time_state = True
if REAL_TIME_FIRST:
w = {
k.replace(".time_faaaa", ".time_first")
if ".time_faaaa" in k
else k: v
(
k.replace(".time_faaaa", ".time_first")
if ".time_faaaa" in k
else k
): v
for k, v in w.items()
}
self.w = w
@@ -631,10 +636,12 @@ class RWKV(MyModule):
torch.cuda.empty_cache()
shape = [i for i in w[x].shape if i != 1]
if len(shape) > 1:
shape = f" {str(shape[0]).rjust(5)} {str(shape[1]).rjust(5)}"
if len(shape) > 2:
shape = f" {str(shape[0]).rjust(5)} {str(shape[1]).rjust(5)} {str(shape[2]).rjust(5)}"
elif len(shape) > 1:
shape = f" {str(shape[0]).rjust(5)} {str(shape[1]).rjust(5)} "
else:
shape = f" {str(shape[0]).rjust(5)} "
shape = f" {str(shape[0]).rjust(5)} "
if layer_id == 0 or layer_id >= args.n_layer - 1:
if print_need_newline:
prxxx("\n", end="")
@@ -2108,16 +2115,25 @@ class RWKV(MyModule):
state[i * 3 + 0] = torch.zeros(
args.n_embd, dtype=atype, requires_grad=False, device=dev
).contiguous()
state[i * 3 + 1] = torch.zeros(
(
args.n_head,
args.n_att // args.n_head,
args.n_att // args.n_head,
),
dtype=torch.float,
requires_grad=False,
device=dev,
).contiguous()
if args.time_state:
state[i * 3 + 1] = (
w[f"blocks.{i}.att.time_state"]
.transpose(1, 2)
.to(dtype=torch.float, device=dev)
.requires_grad_(False)
.contiguous()
)
else:
state[i * 3 + 1] = torch.zeros(
(
args.n_head,
args.n_att // args.n_head,
args.n_att // args.n_head,
),
dtype=torch.float,
requires_grad=False,
device=dev,
).contiguous()
state[i * 3 + 2] = torch.zeros(
args.n_embd, dtype=atype, requires_grad=False, device=dev
).contiguous()

View File

@@ -579,12 +579,13 @@ const ChatPanel: FC = observer(() => {
penalty_decay: commonStore.chatParams.penaltyDecay === defaultPenaltyDecay ? undefined : commonStore.chatParams.penaltyDecay,
user_name: commonStore.activePreset?.userName || undefined,
assistant_name: commonStore.activePreset?.assistantName || undefined,
presystem: commonStore.activePreset?.presystem && undefined
presystem: commonStore.activePreset?.presystem || undefined
}),
signal: chatSseController?.signal,
onmessage(e) {
if (finished) return;
scrollToBottom();
if (!finished && e.data.trim() === '[DONE]') {
if (e.data.trim() === '[DONE]') {
finish();
return;
}
@@ -598,14 +599,15 @@ const ChatPanel: FC = observer(() => {
if (data.model)
commonStore.setLastModelName(data.model);
if (data.choices && Array.isArray(data.choices) && data.choices.length > 0) {
if (!finished && data.choices[0]?.finish_reason) {
finish();
return;
}
answer += data.choices[0]?.delta?.content || '';
commonStore.conversation[answerId!].content = answer;
commonStore.setConversation(commonStore.conversation);
commonStore.setConversationOrder([...commonStore.conversationOrder]);
if (data.choices[0]?.finish_reason) {
finish();
return;
}
}
},
async onopen(response) {

View File

@@ -109,8 +109,9 @@ const CompletionPanel: FC = observer(() => {
}),
signal: completionSseController?.signal,
onmessage(e) {
if (finished) return;
scrollToBottom();
if (!finished && e.data.trim() === '[DONE]') {
if (e.data.trim() === '[DONE]') {
finish();
return;
}
@@ -124,12 +125,13 @@ const CompletionPanel: FC = observer(() => {
if (data.model)
commonStore.setLastModelName(data.model);
if (data.choices && Array.isArray(data.choices) && data.choices.length > 0) {
if (!finished && data.choices[0]?.finish_reason) {
answer += data.choices[0]?.text || data.choices[0]?.delta?.content || '';
setPrompt(prompt + answer.replace(/\s+$/, '') + params.injectEnd.replaceAll('\\n', '\n'));
if (data.choices[0]?.finish_reason) {
finish();
return;
}
answer += data.choices[0]?.text || data.choices[0]?.delta?.content || '';
setPrompt(prompt + answer.replace(/\s+$/, '') + params.injectEnd.replaceAll('\\n', '\n'));
}
},
async onopen(response) {

View File

@@ -214,8 +214,9 @@ const CompositionPanel: FC = observer(() => {
}),
signal: compositionSseController?.signal,
onmessage(e) {
if (finished) return;
scrollToBottom();
if (!finished && e.data.trim() === '[DONE]') {
if (e.data.trim() === '[DONE]') {
finish();
return;
}
@@ -229,12 +230,13 @@ const CompositionPanel: FC = observer(() => {
if (data.model)
commonStore.setLastModelName(data.model);
if (data.choices && Array.isArray(data.choices) && data.choices.length > 0) {
if (!finished && data.choices[0]?.finish_reason) {
answer += data.choices[0]?.text || data.choices[0]?.delta?.content || '';
setPrompt(prompt + answer.replace(/\s+$/, ''));
if (data.choices[0]?.finish_reason) {
finish();
return;
}
answer += data.choices[0]?.text || data.choices[0]?.delta?.content || '';
setPrompt(prompt + answer.replace(/\s+$/, ''));
}
},
async onopen(response) {

View File

@@ -52,7 +52,7 @@ const defaultPreset: Preset = {
stop: '',
injectStart: '',
injectEnd: '',
presystem: true,
presystem: false,
userName: '',
assistantName: ''
};
@@ -275,7 +275,7 @@ const ChatPresetEditor: FC<{
<Labeled flex spaceBetween label={t('Insert default system prompt at the beginning')}
desc={t('Inside the model, there is a default prompt to improve the model\'s handling of common issues, but it may degrade the role-playing effect. You can disable this option to achieve a better role-playing effect.')}
content={
<Switch checked={editingPreset.presystem === undefined ? true : editingPreset.presystem}
<Switch checked={editingPreset.presystem === undefined ? false : editingPreset.presystem}
onChange={(e, data) => {
setEditingPreset({
presystem: data.checked

View File

@@ -207,7 +207,7 @@ export const defaultModelConfigsMac: ModelConfig[] = [
frequencyPenalty: 1
},
modelParameters: {
modelName: 'RWKV-x060-World-1B6-v2-20240208-ctx4096.pth',
modelName: 'RWKV-x060-World-1B6-v2.1-20240328-ctx4096.pth',
device: 'WebGPU',
precision: 'nf4',
storedLayers: 41,
@@ -225,7 +225,7 @@ export const defaultModelConfigsMac: ModelConfig[] = [
frequencyPenalty: 1
},
modelParameters: {
modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
modelName: 'RWKV-x060-World-3B-v2.1-20240417-ctx4096.pth',
device: 'WebGPU',
precision: 'nf4',
storedLayers: 41,
@@ -243,7 +243,7 @@ export const defaultModelConfigsMac: ModelConfig[] = [
frequencyPenalty: 1
},
modelParameters: {
modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
modelName: 'RWKV-x060-World-3B-v2.1-20240417-ctx4096.pth',
device: 'WebGPU',
precision: 'nf4',
storedLayers: 41,
@@ -333,7 +333,7 @@ export const defaultModelConfigsMac: ModelConfig[] = [
frequencyPenalty: 1
},
modelParameters: {
modelName: 'RWKV-x060-World-1B6-v2-20240208-ctx4096.pth',
modelName: 'RWKV-x060-World-1B6-v2.1-20240328-ctx4096.pth',
device: 'MPS',
precision: 'fp32',
storedLayers: 41,
@@ -352,7 +352,7 @@ export const defaultModelConfigsMac: ModelConfig[] = [
frequencyPenalty: 1
},
modelParameters: {
modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
modelName: 'RWKV-x060-World-3B-v2.1-20240417-ctx4096.pth',
device: 'MPS',
precision: 'fp32',
storedLayers: 41,
@@ -371,7 +371,7 @@ export const defaultModelConfigsMac: ModelConfig[] = [
frequencyPenalty: 1
},
modelParameters: {
modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
modelName: 'RWKV-x060-World-3B-v2.1-20240417-ctx4096.pth',
device: 'MPS',
precision: 'fp32',
storedLayers: 41,
@@ -412,7 +412,7 @@ export const defaultModelConfigs: ModelConfig[] = [
frequencyPenalty: 1
},
modelParameters: {
modelName: 'RWKV-x060-World-1B6-v2-20240208-ctx4096.pth',
modelName: 'RWKV-x060-World-1B6-v2.1-20240328-ctx4096.pth',
device: 'CUDA',
precision: 'int8',
storedLayers: 41,
@@ -431,7 +431,7 @@ export const defaultModelConfigs: ModelConfig[] = [
frequencyPenalty: 1
},
modelParameters: {
modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
modelName: 'RWKV-x060-World-3B-v2.1-20240417-ctx4096.pth',
device: 'CUDA',
precision: 'int8',
storedLayers: 6,
@@ -450,7 +450,7 @@ export const defaultModelConfigs: ModelConfig[] = [
frequencyPenalty: 1
},
modelParameters: {
modelName: 'RWKV-x060-World-1B6-v2-20240208-ctx4096.pth',
modelName: 'RWKV-x060-World-1B6-v2.1-20240328-ctx4096.pth',
device: 'CUDA',
precision: 'fp16',
storedLayers: 41,
@@ -469,7 +469,7 @@ export const defaultModelConfigs: ModelConfig[] = [
frequencyPenalty: 1
},
modelParameters: {
modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
modelName: 'RWKV-x060-World-3B-v2.1-20240417-ctx4096.pth',
device: 'CUDA',
precision: 'int8',
storedLayers: 24,
@@ -488,7 +488,7 @@ export const defaultModelConfigs: ModelConfig[] = [
frequencyPenalty: 1
},
modelParameters: {
modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
modelName: 'RWKV-x060-World-3B-v2.1-20240417-ctx4096.pth',
device: 'CUDA',
precision: 'int8',
storedLayers: 24,
@@ -545,7 +545,7 @@ export const defaultModelConfigs: ModelConfig[] = [
frequencyPenalty: 1
},
modelParameters: {
modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
modelName: 'RWKV-x060-World-3B-v2.1-20240417-ctx4096.pth',
device: 'CUDA',
precision: 'int8',
storedLayers: 41,
@@ -564,7 +564,7 @@ export const defaultModelConfigs: ModelConfig[] = [
frequencyPenalty: 1
},
modelParameters: {
modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
modelName: 'RWKV-x060-World-3B-v2.1-20240417-ctx4096.pth',
device: 'CUDA',
precision: 'int8',
storedLayers: 41,
@@ -621,7 +621,7 @@ export const defaultModelConfigs: ModelConfig[] = [
frequencyPenalty: 1
},
modelParameters: {
modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
modelName: 'RWKV-x060-World-3B-v2.1-20240417-ctx4096.pth',
device: 'CUDA',
precision: 'fp16',
storedLayers: 41,
@@ -640,7 +640,7 @@ export const defaultModelConfigs: ModelConfig[] = [
frequencyPenalty: 1
},
modelParameters: {
modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
modelName: 'RWKV-x060-World-3B-v2.1-20240417-ctx4096.pth',
device: 'CUDA',
precision: 'fp16',
storedLayers: 41,
@@ -809,7 +809,7 @@ export const defaultModelConfigs: ModelConfig[] = [
frequencyPenalty: 1
},
modelParameters: {
modelName: 'RWKV-x060-World-1B6-v2-20240208-ctx4096.pth',
modelName: 'RWKV-x060-World-1B6-v2.1-20240328-ctx4096.pth',
device: 'WebGPU',
precision: 'nf4',
storedLayers: 41,
@@ -827,7 +827,7 @@ export const defaultModelConfigs: ModelConfig[] = [
frequencyPenalty: 1
},
modelParameters: {
modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
modelName: 'RWKV-x060-World-3B-v2.1-20240417-ctx4096.pth',
device: 'WebGPU',
precision: 'nf4',
storedLayers: 41,
@@ -845,7 +845,7 @@ export const defaultModelConfigs: ModelConfig[] = [
frequencyPenalty: 1
},
modelParameters: {
modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
modelName: 'RWKV-x060-World-3B-v2.1-20240417-ctx4096.pth',
device: 'WebGPU',
precision: 'nf4',
storedLayers: 41,

View File

@@ -1,5 +1,5 @@
{
"version": "1.7.6",
"version": "1.7.8",
"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)。"
@@ -15,6 +15,27 @@
}
],
"models": [
{
"name": "RWKV-x060-World-1B6-v2.1-20240328-ctx4096.pth",
"desc": {
"en": "RWKV-6 Global Languages 1.6B v2.1",
"zh": "RWKV-6 全球语言 1.6B v2.1",
"ja": "RWKV-6 グローバル言語 1.6B v2.1"
},
"size": 3199845663,
"SHA256": "cda1f0ebe802e2859bfa129546372fd1eac60319111f215f65ced67dd334db36",
"lastUpdated": "2024-03-28T22:57:56",
"url": "https://huggingface.co/BlinkDL/rwkv-6-world/blob/main/RWKV-x060-World-1B6-v2.1-20240328-ctx4096.pth",
"downloadUrl": "https://huggingface.co/BlinkDL/rwkv-6-world/resolve/main/RWKV-x060-World-1B6-v2.1-20240328-ctx4096.pth",
"tags": [
"Official",
"RWKV-6",
"Global",
"Recommended",
"CN",
"JP"
]
},
{
"name": "RWKV-x060-World-1B6-v2-20240208-ctx4096.pth",
"desc": {
@@ -33,6 +54,28 @@
"Global",
"CN",
"JP"
],
"hide": true
},
{
"name": "RWKV-x060-World-3B-v2.1-20240417-ctx4096.pth",
"desc": {
"en": "RWKV-6 Global Languages 3B v2.1",
"zh": "RWKV-6 全球语言 3B v2.1",
"ja": "RWKV-6 グローバル言語 3B v2.1"
},
"size": 6199859158,
"SHA256": "c7c4e68ea6d561661de0562c8f229e62668f9c2d804346a87ae28d6209cc4738",
"lastUpdated": "2024-04-17T15:58:38",
"url": "https://huggingface.co/BlinkDL/rwkv-6-world/blob/main/RWKV-x060-World-3B-v2.1-20240417-ctx4096.pth",
"downloadUrl": "https://huggingface.co/BlinkDL/rwkv-6-world/resolve/main/RWKV-x060-World-3B-v2.1-20240417-ctx4096.pth",
"tags": [
"Official",
"RWKV-6",
"Global",
"Recommended",
"CN",
"JP"
]
},
{
@@ -51,10 +94,10 @@
"Official",
"RWKV-6",
"Global",
"Recommended",
"CN",
"JP"
]
],
"hide": true
},
{
"name": "RWKV-5-World-0.1B-v1-20230803-ctx4096.pth",
@@ -167,7 +210,6 @@
"Official",
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"Global",
"Recommended",
"CN",
"JP"
]
@@ -188,7 +230,6 @@
"Official",
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"Global",
"Recommended",
"CN",
"JP"
]
@@ -268,8 +309,7 @@
"Finetuned",
"RWKV-5",
"Global",
"Role Play",
"Recommended"
"Role Play"
],
"customTokenizer": "backend-python/rwkv_pip/rwkv_vocab_v20230424_special_token.txt"
},
@@ -288,8 +328,7 @@
"tags": [
"Finetuned",
"RWKV-5",
"Global",
"Recommended"
"Global"
],
"customTokenizer": "backend-python/rwkv_pip/rwkv_vocab_v20230424_special_token.txt"
},
@@ -309,7 +348,26 @@
"Finetuned",
"RWKV-5",
"Global",
"Recommended",
"CN",
"JP"
]
},
{
"name": "Mobius-r5-chat-12b-8k-0426.pth",
"desc": {
"en": "RWKV-5 Mobius 12B Ctx8k",
"zh": "RWKV-5 Mobius 12B 8k上下文",
"ja": "RWKV-5 Mobius 12B 8kコンテキスト"
},
"size": 23157425346,
"SHA256": "b5304bdae6026c93ab5ab970edbc70cd35c2ed3b3ce4cd906996116681681b83",
"lastUpdated": "2024-04-27T09:03:39",
"url": "https://huggingface.co/TimeMobius/Mobius-RWKV-r5-chat-12B-8k/blob/main/Mobius-r5-chat-12b-8k-0426.pth",
"downloadUrl": "https://huggingface.co/TimeMobius/Mobius-RWKV-r5-chat-12B-8k/resolve/main/Mobius-r5-chat-12b-8k-0426.pth",
"tags": [
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"RWKV-5",
"Global",
"CN",
"JP"
]