quangbk / generativeagent_llm Goto Github PK

Loading model ...
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In[36], line 5
      1 # MODEL_PATH = '/home/quang/working/LLMs/oobabooga_linux/text-generation-webui/models/TheBloke_Wizard-Vicuna-7B-Uncensored-GPTQ'
      2 # CHECKPOINT_PATH = '/home/quang/working/LLMs/oobabooga_linux/text-generation-webui/models/TheBloke_Wizard-Vicuna-7B-Uncensored-GPTQ/Wizard-Vicuna-7B-Uncensored-GPTQ-4bit-128g.no-act-order.safetensors'
      4 DEVICE = torch.device('cuda:0')
----> 5 model, tokenizer = load_model_main(MODEL_PATH, CHECKPOINT_PATH, DEVICE)

File /mnt/data/generativeAgent_LLM/server/model.py:56, in load_model_main(model_para, checkpoint_para, device)
     55 def load_model_main(model_para, checkpoint_para, device):
---> 56     model = load_quant(model_para, checkpoint_para, 4, 128)
     57     model.to(device)
     58     tokenizer = AutoTokenizer.from_pretrained(model_para)

File /mnt/data/generativeAgent_LLM/server/model.py:47, in load_quant(model, checkpoint, wbits, groupsize)
     45 if checkpoint.endswith('.safetensors'):
     46     from safetensors.torch import load_file as safe_load
---> 47     model.load_state_dict(safe_load(checkpoint))
     48 else:
     49     model.load_state_dict(torch.load(checkpoint))

File ~/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py:1671, in Module.load_state_dict(self, state_dict, strict)
   1666         error_msgs.insert(
   1667             0, 'Missing key(s) in state_dict: {}. '.format(
   1668                 ', '.join('"{}"'.format(k) for k in missing_keys)))
   1670 if len(error_msgs) > 0:
-> 1671     raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
   1672                        self.__class__.__name__, "\n\t".join(error_msgs)))
   1673 return _IncompatibleKeys(missing_keys, unexpected_keys)

RuntimeError: Error(s) in loading state_dict for LlamaForCausalLM:
	Unexpected key(s) in state_dict: "model.layers.0.self_attn.rotary_emb.inv_freq", "model.layers.0.self_attn.k_proj.g_idx", "model.layers.0.self_attn.o_proj.g_idx", "model.layers.0.self_attn.q_proj.g_idx", "model.layers.0.self_attn.v_proj.g_idx", "model.layers.0.mlp.down_proj.g_idx", "model.layers.0.mlp.gate_proj.g_idx", "model.layers.0.mlp.up_proj.g_idx", "model.layers.1.self_attn.rotary_emb.inv_freq", "model.layers.1.self_attn.k_proj.g_idx", "model.layers.1.self_attn.o_proj.g_idx", "model.layers.1.self_attn.q_proj.g_idx", "model.layers.1.self_attn.v_proj.g_idx", "model.layers.1.mlp.down_proj.g_idx", "model.layers.1.mlp.gate_proj.g_idx", "model.layers.1.mlp.up_proj.g_idx", "model.layers.2.self_attn.rotary_emb.inv_freq", "model.layers.2.self_attn.k_proj.g_idx", "model.layers.2.self_attn.o_proj.g_idx", "model.layers.2.self_attn.q_proj.g_idx", "model.layers.2.self_attn.v_proj.g_idx", "model.layers.2.mlp.down_proj.g_idx", "model.layers.2.mlp.gate_proj.g_idx",

### Instruction:
{{recent_memories}}

### Input:
Given only the information above, what are 3 most salient high-level questions we can answer about the subjects in the statements?

### Response:
{{#geneach 'items' num_iterations=3}}{{gen 'this' top_k=30 top_p=0.18 repetition_penalty=1.15 temperature=1.99 stop='
'}}
{{/geneach}}

╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ in <cell line: 6>:6                                                                              │
│                                                                                                  │
│ /content/generativeAgent_LLM/server/generativeAgent.py:122 in add_memories                       │
│                                                                                                  │
│   119 │   │                                                                                      │
│   120 │   │   if not self.reflecting and self.aggregate_importance > self.reflection_threshold   │
│   121 │   │   │   self.reflecting = True                                                         │
│ ❱ 122 │   │   │   self._relection()                                                              │
│   123 │   │   │   self.aggregate_importance = 0.0                                                │
│   124 │   │   │   self.reflecting = False                                                        │
│   125                                                                                            │
│                                                                                                  │
│ /content/generativeAgent_LLM/server/generativeAgent.py:144 in _relection                         │
│                                                                                                  │
│   141 │   │   return result['items']                                                             │
│   142 │                                                                                          │
│   143 │   def _relection(self,):                                                                 │
│ ❱ 144 │   │   list_salient = self._get_salient()                                                 │
│   145 │   │   list_docs = []                                                                     │
│   146 │   │   for salient_temp in list_salient:                                                  │
│   147 │   │   │   docs = self.retriever.get_relevant_documents(salient_temp, self.get_current_   │
│                                                                                                  │
│ /content/generativeAgent_LLM/server/generativeAgent.py:134 in _get_salient                       │
│                                                                                                  │
│   131 │   │                                                                                      │
│   132 │   │   prompt = self.guidance(PROMPT_SALIENT, silent=self.silent)                         │
│   133 │   │   result = prompt(recent_memories=recent_memories_text)                              │
│ ❱ 134 │   │   return result['items']                                                             │
│   135 │                                                                                          │
│   136 │   def _get_insights(self, list_docs):                                                    │
│   137 │   │   docs = list_docs                                                                   │
│                                                                                                  │
│ /usr/local/lib/python3.10/dist-packages/guidance/_program.py:470 in __getitem__                  │
│                                                                                                  │
│   467 │   │   │   self._execute_complete.set()                                                   │
│   468 │                                                                                          │
│   469 │   def __getitem__(self, key):                                                            │
│ ❱ 470 │   │   return self._variables[key]                                                        │
│   471 │                                                                                          │
│   472 │   def __contains__(self, key):                                                           │
│   473 │   │   return key in self._variables                                                      │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
KeyError: 'items'

### Instruction:
{{recent_memories}}

### Input:
Given only the information above, what are 3 most salient high-level questions we can answer about the subjects in the statements?

### Response:
{{#geneach 'items' num_iterations=3}}{{gen 'this' top_k=30 top_p=0.18 repetition_penalty=1.15 temperature=1.99 stop='
'}}
{{/geneach}}

sam_observations = [
    "Sam wake up in the morning",
]
sam.add_memories(sam_observations)

summary = sam.get_summary(force_refresh=True)

╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ in <cell line: 1>:1                                                                              │
│                                                                                                  │
│ /content/generativeAgent_LLM/server/generativeAgent.py:163 in get_summary                        │
│                                                                                                  │
│   160 │   │   │   or since_refresh >= self.summary_refresh_seconds                               │
│   161 │   │   │   or force_refresh                                                               │
│   162 │   │   ):                                                                                 │
│ ❱ 163 │   │   │   core_characteristics = self._run_characteristics()                             │
│   164 │   │   │   daily_occupation = self._run_occupation()                                      │
│   165 │   │   │   feeling = self._run_feeling()                                                  │
│   166                                                                                            │
│                                                                                                  │
│ /content/generativeAgent_LLM/server/generativeAgent.py:173 in _run_characteristics               │
│                                                                                                  │
│   170 │   │   return self.summary                                                                │
│   171 │                                                                                          │
│   172 │   def _run_characteristics(self,):                                                       │
│ ❱ 173 │   │   docs = self.retriever.get_relevant_documents(self.name + "'s core characteristic   │
│   174 │   │   statements = get_text_from_docs(docs, include_time = False)                        │
│   175 │   │                                                                                      │
│   176 │   │   prompt = self.guidance(PROMPT_CHARACTERISTICS, silent=self.silent)                 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
TypeError: BaseRetriever.get_relevant_documents() takes 2 positional arguments but 3 were given

with mock_now(datetime.datetime(2011, 2, 3, 10, 11)):
    print(sam.retriever.get_relevant_documents("sam's core characteristics")

[Document(page_content='Sam is a Ph.D student, his major is CS', metadata={'importance': 7, 'created_at': datetime.datetime(2023, 7, 19, 7, 25, 42, 649173), 'last_accessed_at': MockDateTime(2011, 2, 3, 10, 11), 'buffer_idx': 0}), Document(page_content='Sam likes computer', metadata={'importance': 1, 'created_at': datetime.datetime(2023, 7, 19, 7, 25, 42, 649173), 'last_accessed_at': MockDateTime(2011, 2, 3, 10, 11), 'buffer_idx': 1}), Document(page_content='Wants to get his AI company to be successful.', metadata={'importance': 8, 'created_at': datetime.datetime(2023, 7, 19, 7, 25, 42, 649173), 'last_accessed_at': MockDateTime(2011, 2, 3, 10, 11), 'buffer_idx': 5}), Document(page_content='Sam really wants to get to work to get the marketing project for his AI company done', metadata={'importance': 3, 'created_at': datetime.datetime(2023, 7, 19, 7, 25, 42, 649173), 'last_accessed_at': MockDateTime(2011, 2, 3, 10, 11), 'buffer_idx': 7}), Document(page_content='Sam wake up in the morning', metadata={'importance': 1, 'created_at': datetime.datetime(2023, 7, 19, 7, 25, 42, 649173), 'last_accessed_at': MockDateTime(2011, 2, 3, 10, 11), 'buffer_idx': 6}), Document(page_content='Sam has a dog, named Max', metadata={'importance': 1, 'created_at': datetime.datetime(2023, 7, 19, 7, 25, 42, 649173), 'last_accessed_at': MockDateTime(2011, 2, 3, 10, 11), 'buffer_idx': 4}), Document(page_content="Sam's farther is a doctor", metadata={'importance': 1, 'created_at': datetime.datetime(2023, 7, 19, 7, 25, 42, 649173), 'last_accessed_at': MockDateTime(2011, 2, 3, 10, 11), 'buffer_idx': 3}), Document(page_content='Sam lives with his friend, Bob', metadata={'importance': 1, 'created_at': datetime.datetime(2023, 7, 19, 7, 25, 42, 649173), 'last_accessed_at': MockDateTime(2011, 2, 3, 10, 11), 'buffer_idx': 2})]