题外话

我皈依Arch啦！！！！！

让我们一起开ArchLinux创创卡车罢（x

猫猫金句：

这是我在高一更新的最后一篇文章 当然，宁波外国语学校生存指北 也会得到高一最后一次更新（x

为什么我笔记本运存升20G了？因为我忍不住下手买了个229的kingston，还花了骨折价买了199的500G三星Pro 880（乐

chi的450，没力！

有喜必有悲：我的所有workspace数据全被清了，现在真的是什么都没有了（大悲

chiOpenID的源码也空了 我只好重构。

这件事情告诉我们：备份是很重要的。

mujitogawa大佬您强强！去参加人工智能国赛了！

1. rwkv简述

RWKV是一种新的神经网络模型，它结合了RNN和Transformer的优点，可以实现高效的并行训练和推理 。RWKV的全称是Receptance Weighted Key Value，意思是接纳权重键值 。RWKV是由彭博提出的研究项目，并已经被集成到Hugging Face的transformers库中。 
— by edgeGPT

再说一嘴，Peng Bo大大是中国人，还有训练模型是开源的！！！(在这里批评某小软件公司)而且RWKV只要运存8G+就可以运行，只是快慢的区别罢了。

2. 事前准备

你需要：

1. 脑子
2. 一台运存不低于8G，硬盘存储剩余空间不低于40G的电脑 系统不限，我们这里以Linux系统为例
3. git，Python，pip（请自行搜索安装配源） miniconda/canconda也可，要求是你会用
4. 预训练模型
5. chatRWKV环境

预训练模型可以从这里取得：https://huggingface.co/BlinkDL/rwkv-4-raven/blob/main/RWKV-4-Raven-7B-v11-Eng49%25-Chn49%25-Jpn1%25-Other1%25-20230430-ctx8192.pth 直接点Download即可

这是RWKV4 raven(擅长与人打交道 相当于ChatGPT)的中文模型 14.8G huggingface的网速还是不错的，但是还是得等一个小时（建议aria2下载）

如果你不想用这个预训练模型可以切换其他模型，或者是切换更小的模型：https://huggingface.co/BlinkDL/

各个模型解释如下：

Raven 模型：适合直接聊天，适合 +i 指令。有很多种语言的版本，看清楚用哪个。适合聊天、完成任务、写代码。可以作为任务去写文稿、大纲、故事、诗歌等等，但文笔不如 testNovel 系列模型。
Novel-ChnEng 模型：中英文小说模型，可以用 +gen 生成世界设定（如果会写 prompt，可以控制下文剧情和人物），可以写科幻奇幻。不适合聊天，不适合 +i 指令。
Novel-Chn 模型：纯中文网文模型，只能用 +gen 续写网文（不能生成世界设定等等），但是写网文写得更好（也更小白文，适合写男频女频）。不适合聊天，不适合 +i 指令。
Novel-ChnEng-ChnPro 模型：将 Novel-ChnEng 在高质量作品微调（名著，科幻，奇幻，古典，翻译，等等）。

来自RWKV作者 Peng Bo 的 知乎专栏<https://zhuanlan.zhihu.com/p/618011122>

3. 食用

我们需要搭建一个gradio环境

首先我们需要clone来自huggingface的git repo

git clone https://huggingface.co/spaces/BlinkDL/Raven-RWKV-7B

然后再使用pip安装依赖

cd Raven-RWKV-7B
python -m pip install --upgrade pip -i https://pypi.tuna.tsinghua.edu.cn/simple

如果没用施法可以把 -i 以及后面的去掉

成功了之后，编辑app.py

import gradio as gr
import os, gc, copy, torch
from datetime import datetime
from huggingface_hub import hf_hub_download
from pynvml import *
# 有nvidia显卡取消‘#’
#nvmlInit()
#gpu_h = nvmlDeviceGetHandleByIndex(0)
ctx_limit = 1536

os.environ["RWKV_JIT_ON"] = '1'
# 如果有nvidia显卡的话就1
os.environ["RWKV_CUDA_ON"] = '0' # if '1' then use CUDA kernel for seq mode (much faster)

from rwkv.model import RWKV

model = RWKV(model='/path/to/module, strategy='selection')
from rwkv.utils import PIPELINE, PIPELINE_ARGS
pipeline = PIPELINE(model, "20B_tokenizer.json")

def generate_prompt(instruction, input=None):
 instruction = instruction.strip().replace('\r
','
').replace('

','
')
 input = input.strip().replace('\r
','
').replace('

','
')
 if input:
 return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

# Instruction:
{instruction}

# Input:
{input}

# Response:
"""
 else:
 return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.

# Instruction:
{instruction}

# Response:
"""

def evaluate(
 instruction,
 input=None,
 token_count=200,
 temperature=1.0,
 top_p=0.7,
 presencePenalty = 0.1,
 countPenalty = 0.1,
):
 args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
 alpha_frequency = countPenalty,
 alpha_presence = presencePenalty,
 token_ban = [], # ban the generation of some tokens
 token_stop = [0]) # stop generation whenever you see any token here

 instruction = instruction.strip().replace('\r
','
').replace('

','
')
 input = input.strip().replace('\r
','
').replace('

','
')
 ctx = generate_prompt(instruction, input)
 
 all_tokens = []
 out_last = 0
 out_str = ''
 occurrence = {}
 state = None
 for i in range(int(token_count)):
 out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:] if i == 0 else [token], state)
 for n in occurrence:
 out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)

 token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
 if token in args.token_stop:
 break
 all_tokens += [token]
 if token not in occurrence:
 occurrence[token] = 1
 else:
 occurrence[token] += 1
 
 tmp = pipeline.decode(all_tokens[out_last:])
 if '\ufffd' not in tmp:
 out_str += tmp
 yield out_str.strip()
 out_last = i + 1
 # 有nvidia显卡取消‘#’
 #gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
 #print(f'vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}') 
 del out
 del state
 gc.collect()
 torch.cuda.empty_cache()
 yield out_str.strip()

examples = [
 ["Tell me about ravens.", "", 300, 1.2, 0.5, 0.4, 0.4],
 ["Write a python function to mine 1 BTC, with details and comments.", "", 300, 1.2, 0.5, 0.4, 0.4],
 ["Write a song about ravens.", "", 300, 1.2, 0.5, 0.4, 0.4],
 ["Explain the following metaphor: Life is like cats.", "", 300, 1.2, 0.5, 0.4, 0.4],
 ["Write a story using the following information", "A man named Alex chops a tree down", 300, 1.2, 0.5, 0.4, 0.4],
 ["Generate a list of adjectives that describe a person as brave.", "", 300, 1.2, 0.5, 0.4, 0.4],
 ["You have $100, and your goal is to turn that into as much money as possible with AI and Machine Learning. Please respond with detailed plan.", "", 300, 1.2, 0.5, 0.4, 0.4],
]

##########################################################################

chat_intro = '''The following is a coherent verbose detailed conversation between <|user|> and an AI girl named <|bot|>.

<|user|>: Hi <|bot|>, Would you like to chat with me for a while?

<|bot|>: Hi <|user|>. Sure. What would you like to talk about? I'm listening.
'''

def user(message, chatbot):
 chatbot = chatbot or []
 # print(f"User: {message}")
 return "", chatbot + [[message, None]]

def alternative(chatbot, history):
 if not chatbot or not history:
 return chatbot, history
 
 chatbot[-1][1] = None
 history[0] = copy.deepcopy(history[1])

 return chatbot, history

def chat(
 prompt,
 user,
 bot,
 chatbot,
 history,
 temperature=1.0,
 top_p=0.8,
 presence_penalty=0.1,
 count_penalty=0.1,
):
 args = PIPELINE_ARGS(temperature=max(0.2, float(temperature)), top_p=float(top_p),
 alpha_frequency=float(count_penalty),
 alpha_presence=float(presence_penalty),
 token_ban=[], # ban the generation of some tokens
 token_stop=[]) # stop generation whenever you see any token here
 
 if not chatbot:
 return chatbot, history

 message = chatbot[-1][0]
 message = message.strip().replace('\r
','
').replace('

','
')
 ctx = f"{user}: {message}

{bot}:"

 if not history:
 prompt = prompt.replace("<|user|>", user.strip())
 prompt = prompt.replace("<|bot|>", bot.strip())
 prompt = prompt.strip()
 prompt = f"
{prompt}

"

 out, state = model.forward(pipeline.encode(prompt), None)
 history = [state, None, []] # [state, state_pre, tokens]
 # print("History reloaded.")

 [state, _, all_tokens] = history
 state_pre_0 = copy.deepcopy(state)

 out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:], state)
 state_pre_1 = copy.deepcopy(state) # For recovery

 # print("Bot:", end='')

 begin = len(all_tokens)
 out_last = begin
 out_str: str = ''
 occurrence = {}
 for i in range(300):
 if i <= 0:
 nl_bias = -float('inf')
 elif i <= 30:
 nl_bias = (i - 30) * 0.1
 elif i <= 130:
 nl_bias = 0
 else:
 nl_bias = (i - 130) * 0.25
 out[187] += nl_bias
 for n in occurrence:
 out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)

 token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
 next_tokens = [token]
 if token == 0:
 next_tokens = pipeline.encode('

')
 all_tokens += next_tokens

 if token not in occurrence:
 occurrence[token] = 1
 else:
 occurrence[token] += 1

 out, state = model.forward(next_tokens, state)

 tmp = pipeline.decode(all_tokens[out_last:])
 if '\ufffd' not in tmp:
 # print(tmp, end='', flush=True)
 out_last = begin + i + 1
 out_str += tmp

 chatbot[-1][1] = out_str.strip()
 history = [state, all_tokens]
 yield chatbot, history

 out_str = pipeline.decode(all_tokens[begin:])
 out_str = out_str.replace("\r
", '
').replace('\
', '
')

 if '

' in out_str:
 break

 # State recovery
 if f'{user}:' in out_str or f'{bot}:' in out_str:
 idx_user = out_str.find(f'{user}:')
 idx_user = len(out_str) if idx_user == -1 else idx_user
 idx_bot = out_str.find(f'{bot}:')
 idx_bot = len(out_str) if idx_bot == -1 else idx_bot
 idx = min(idx_user, idx_bot)

 if idx < len(out_str):
 out_str = f" {out_str[:idx].strip()}

"
 tokens = pipeline.encode(out_str)

 all_tokens = all_tokens[:begin] + tokens
 out, state = model.forward(tokens, state_pre_1)
 break
 # 有nvidia显卡取消‘#’
 #gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
 #print(f'vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}') 

 gc.collect()
 torch.cuda.empty_cache()

 chatbot[-1][1] = out_str.strip()
 history = [state, state_pre_0, all_tokens]
 yield chatbot, history

##########################################################################

with gr.Blocks(title=title) as demo:
 gr.HTML(f"<div style=\"text-align: center;\">
<h1>🐦Raven - {title}</h1>
</div>")
 with gr.Tab("Instruct mode"):
 gr.Markdown(f"Raven is [RWKV 7B](https://github.com/BlinkDL/ChatRWKV) 100% RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM) finetuned to follow instructions. *** Please try examples first (bottom of page) *** (edit them to use your question). Demo limited to ctxlen {ctx_limit}. Finetuned on alpaca, gpt4all, codealpaca and more. For best results, *** keep you prompt short and clear ***. <b>UPDATE: now with Chat (see above, as a tab) ==> turn off as of now due to VRAM leak caused by buggy code.</b>.")
 with gr.Row():
 with gr.Column():
 instruction = gr.Textbox(lines=2, label="Instruction", value="Tell me about ravens.")
 input = gr.Textbox(lines=2, label="Input", placeholder="none")
 token_count = gr.Slider(10, 300, label="Max Tokens", step=10, value=300)
 temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.2)
 top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.5)
 presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0.4)
 count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.4)
 with gr.Column():
 with gr.Row():
 submit = gr.Button("Submit", variant="primary")
 clear = gr.Button("Clear", variant="secondary")
 output = gr.Textbox(label="Output", lines=5)
 data = gr.Dataset(components=[instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty], samples=examples, label="Example Instructions", headers=["Instruction", "Input", "Max Tokens", "Temperature", "Top P", "Presence Penalty", "Count Penalty"])
 submit.click(evaluate, [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty], [output])
 clear.click(lambda: None, [], [output])
 data.click(lambda x: x, [data], [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty])
 
 # with gr.Tab("Chat (Experimental - Might be buggy - use ChatRWKV for reference)"):
 # gr.Markdown(f'''<b>*** The length of response is restricted in this demo. Use ChatRWKV for longer generations. ***</b> Say "go on" or "continue" can sometimes continue the response. If you'd like to edit the scenario, make sure to follow the exact same format: empty lines between (and only between) different speakers. Changes only take effect after you press [Clear]. <b>The default "Bob" & "Alice" names work the best.</b>''', label="Description")
 # with gr.Row():
 # with gr.Column():
 # chatbot = gr.Chatbot()
 # state = gr.State()
 # message = gr.Textbox(label="Message", value="Write me a python code to land on moon.")
 # with gr.Row():
 # send = gr.Button("Send", variant="primary")
 # alt = gr.Button("Alternative", variant="secondary")
 # clear = gr.Button("Clear", variant="secondary")
 # with gr.Column():
 # with gr.Row():
 # user_name = gr.Textbox(lines=1, max_lines=1, label="User Name", value="Bob")
 # bot_name = gr.Textbox(lines=1, max_lines=1, label="Bot Name", value="Alice")
 # prompt = gr.Textbox(lines=10, max_lines=50, label="Scenario", value=chat_intro)
 # temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.2)
 # top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.5)
 # presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0.4)
 # count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.4)
 # chat_inputs = [
 # prompt,
 # user_name,
 # bot_name,
 # chatbot,
 # state,
 # temperature,
 # top_p,
 # presence_penalty,
 # count_penalty
 # ]
 # chat_outputs = [chatbot, state]
 # message.submit(user, [message, chatbot], [message, chatbot], queue=False).then(chat, chat_inputs, chat_outputs)
 # send.click(user, [message, chatbot], [message, chatbot], queue=False).then(chat, chat_inputs, chat_outputs)
 # alt.click(alternative, [chatbot, state], [chatbot, state], queue=False).then(chat, chat_inputs, chat_outputs)
 # clear.click(lambda: ([], None, ""), [], [chatbot, state, message], queue=False)

demo.queue(concurrency_count=1, max_size=10)
demo.launch(share=False)

上面会开启一个webserver，如果不能正常运行，可以使用6GB模型：https://huggingface.co/BlinkDL/rwkv-4-raven/blob/main/RWKV-4-Raven-3B-v12-Eng49%25-Chn49%25-Jpn1%25-Other1%25-20230527-ctx4096.pth

在我这里6GB模型占用了10GB的运存，生成速度也不算理想，14G电脑直接炸了

4. rwkv.cpp救场

如果rwkv上面的代码没办法在你的电脑上运行，那么你也可以考虑一下使用rwkv.cpp进行优化，减少占用。

先clone下rwkv.cpp的仓库：

git clone --recursive https://github.com/saharNooby/rwkv.cpp.git
cd rwkv.cpp

然后转换模型，如果出现错误请进入rwkv文件夹安装依赖项

python rwkv/convert_pytorch_to_ggml.py /path/to/file ./rwkv.cpp-7B.bin float16

如果找不到自己的文件地址，windows11可以右键文件，复制文件路径。

在量化模型前，需要安装依赖库,在GitHub Releases

请把文件拷贝到根目录/rwkv/中

然后量化模型：

python rwkv/quantize.py ./rwkv.cpp-7B.bin ./rwkv.cpp-7B-Q4_1_O.bin 4

最后可以食用啦

python rwkv/chat_with_bot.py ./rwkv.cpp-7B-Q4_1_O.bin

实测占用5-6G运存，生成速度也能接受（2-3s一个中文字）

4. 总结

RWKV总体上是一个很不错的语言模型，买到了1660Ti就训练你啦（x