This is a state for rwkv6_7b_v2.1 that generates a summary about entities and relations between them

The input is solely the context that you want this model to analyze
The output are domain, expert role in this domain and specific tasks that this export can do in a jsonl format.

Please refer to the following demo as test code:

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/entity_summary/entity_summary.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 ='请阅读iput中的entities和descritions，围绕entity和descrition写一个简单的200字介绍，介绍需要包括所的entity和他们之间的关系.最终内容不能超过200字'
input_text = '"entities": ["汉语", "语义偏移", "构式语法", "评价性语境", "词汇意义"], "descriptions": ["汉语是中国的主要语言，具有丰富的语义结构和复杂的语法体系。", "语义偏移是指在特定语境下，词语的意义发生的变化或偏离。", "构式语法研究的是句子结构的模式及其功能，是语言学的一个分支。", "评价性语境指的是包含情感色彩或评价性质的语言环境，影响着语言表达的意义。", "词汇意义指的是单词在特定语境下的具体含义，可以因语境而变化。"]'
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 = 1.3, top_p = 0.5, top_k = 0, # top_k = 0 then ignore
                     alpha_frequency = 0.7,
                     alpha_presence = 0.5,
                     alpha_decay = 0.996, # gradually decay the penalty
                     token_ban = [0], # ban the generation of some tokens
                     token_stop = [bot_char], # 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=200, args=args, callback=my_print,state=states_value)
print('\n')

The final printed input and output: