| # 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: | |
| ```python | |
| 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: | |
|  |