RWKVGraphRAGStates
Collection
This collection contains all RWKV-6-7B states for GraphRag
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6 items
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Updated
from rwkv.model import RWKV
from rwkv.utils import PIPELINE, PIPELINE_ARGS
import torch
# download models: https://huggingface.co/BlinkDL
model = RWKV(model='/home/rwkv/Peter/model/base/RWKV-x060-World-7B-v2.1-20240507-ctx4096.pth', strategy='cuda fp16')
print(model.args)
pipeline = PIPELINE(model, "rwkv_vocab_v20230424") # 20B_tokenizer.json is in https://github.com/BlinkDL/ChatRWKV
# use pipeline = PIPELINE(model, "rwkv_vocab_v20230424") for rwkv "world" models
states_file = '/home/rwkv/Peter/rwkv_graphrag/agents/persona_domain_states/RWKV-x060-World-7B-v2.1-20240507-ctx4096.pth.pth'
states = torch.load(states_file)
states_value = []
device = 'cuda'
n_head = model.args.n_head
head_size = model.args.n_embd//model.args.n_head
for i in range(model.args.n_layer):
key = f'blocks.{i}.att.time_state'
value = states[key]
prev_x = torch.zeros(model.args.n_embd,device=device,dtype=torch.float16)
prev_states = value.clone().detach().to(device=device,dtype=torch.float16).transpose(1,2)
prev_ffn = torch.zeros(model.args.n_embd,device=device,dtype=torch.float16)
states_value.append(prev_x)
states_value.append(prev_states)
states_value.append(prev_ffn)
cat_char = '🐱'
bot_char = '🤖'
instruction ='根据input中的领域和任务,协助用户识别input文本中存在的实体类型。 实体类型必须与用户任务相关。 避免使用诸如“其他”或“未知”的通用实体类型。 非常重要的是:不要生成冗余或重叠的实体类型。用JSON格式输出。'
input_text = '有个空空道人访道求仙,从大荒山无稽崖青埂峰下经过,忽见一大块石上字迹分明,编述历历,《石头记》是也。空空道人将《石头记》抄录下来,改名为《情僧录》。至吴玉峰题曰《红楼梦》。东鲁孔梅溪则题曰《风月宝鉴》。后因曹雪芹于悼红轩中披阅十载,增删五次,纂成目录,分出章回,则题曰《金陵十二钗》。姑苏乡宦甄士隐梦见一僧一道携无缘补天之石(通灵宝玉)下凡历练,又讲绛珠仙子为报神瑛侍者浇灌之恩追随神瑛侍者下世为人,以泪报恩。梦醒后,抱女儿英莲去看“过会”[2]。甄士隐结交并接济了寄居于隔壁葫芦庙内的胡州人氏贾化(号雨村)。某日,贾雨村造访甄士隐,无意中遇见甄家丫鬟娇杏,以为娇杏对其有意。中秋时节,甄士隐于家中宴请贾雨村,得知贾雨村的抱负后,赠银送衣以作贾雨村上京赴考之盘缠,第二天,贾雨村不辞而别便上路赴考。第二年元宵佳节当晚,甄家仆人霍启在看社火花灯时,不慎丢失了甄士隐唯一的女儿英莲[3]。三月十五日,葫芦庙失火祸及甄家,落魄的甄士隐带家人寄居于如州岳丈封肃家中,后遇一僧一道,悟出《好了歌》真谛,随僧道而去。'
ctx = f'{cat_char}:{instruction}\n{input_text}\n{bot_char}:'
print(ctx)
def my_print(s):
print(s, end='', flush=True)
args = PIPELINE_ARGS(temperature = 1, top_p = 0.2, top_k = 0, # top_k = 0 then ignore
alpha_frequency = 0.5,
alpha_presence = 0.5,
alpha_decay = 0.998, # gradually decay the penalty
token_ban = [0], # ban the generation of some tokens
token_stop = [0,1], # stop generation whenever you see any token here
chunk_len = 256) # split input into chunks to save VRAM (shorter -> slower)
pipeline.generate(ctx, token_count=1000, args=args, callback=my_print,state=states_value)
print('\n')