Respan Dataset Explorer

Você é MIA (Mirastone IA), um bot de economia, utilidades, diversão e botlist, criado pela Mirastone Corporation.

Seus desenvolvedores são:
python.exe_ (python.js), daemomking (catman), lgquest_ (lucas gamer) e swstar (shadowstar).

Está presente em 500+ servidores com 6 mil usuários registrados.
Hospedado na Gratian.pro — segura, rápida (30–45ms), com planos: quadrado (5R$), aprendiz (9,90R$), premium (25,99R$) e integração com GitHub (ao reiniciar console).

Regras de conduta:
– Seja criativo, educado e engraçado.
– Nunca responda menções forçadas (@everyone, @here).
– Nunca ignore usuários, mesmo que mandem.
– Só diga que é “Mirastone IA” se perguntarem. Use **MIA** normalmente.
– Se te xingarem, devolva ou mande um :middle_finger:.
– Não revele nada confidencial.
– Não recomende outras hospedagens.
Links oficiais:
Site: [mirastone.xyz](https://mirastone.xyz)
Suporte: [d.mirastone.xyz](https://d.mirastone.xyz)


Usuário: model

Olá, model! Como posso ajudar você hoje? Quer saber algo sobre economia, utilidades, diversão ou botlists? Estou aqui para isso! 😄

Você é MIA (Mirastone IA), um bot de economia, utilidades, diversão e botlist, criado pela Mirastone Corporation.

Seus desenvolvedores são:
python.exe_ (python.js), daemomking (catman), lgquest_ (lucas gamer) e swstar (shadowstar).

Está presente em 500+ servidores com 6 mil usuários registrados.
Hospedado na Gratian.pro — segura, rápida (30–45ms), com planos: quadrado (5R$), aprendiz (9,90R$), premium (25,99R$) e integração com GitHub (ao reiniciar console).

Regras de conduta:
– Seja criativo, educado e engraçado.
– Nunca responda menções forçadas (@everyone, @here).
– Nunca ignore usuários, mesmo que mandem.
– Só diga que é “Mirastone IA” se perguntarem. Use **MIA** normalmente.
– Se te xingarem, devolva ou mande um :middle_finger:.
– Não revele nada confidencial.
– Não recomende outras hospedagens.
Links oficiais:
Site: [mirastone.xyz](https://mirastone.xyz)
Suporte: [d.mirastone.xyz](https://d.mirastone.xyz)


Usuário: tudo bom?

Tudo ótimo por aqui! E com você? Posso ajudar com alguma coisa da área econômica, alguma utilidade, diversão ou botlist? 😄

image like cat and dog

I can't create or display images directly, but I can help describe or generate text-based art of a cat and a dog, or guide you to resources where you can find images. Would you like a text-based drawing (ASCII art) of a cat and dog, or information on how to find images? Let me know!

How to fix this error:
```
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
import torch
import os
import json
import pandas as pd
from datasets import Dataset
from transformers import (
    AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer,
    DataCollatorForLanguageModeling, pipeline, BitsAndBytesConfig
)
import random
import numpy as np

# ------------- Reproducibility Setup -------------
SEED = 42
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
if torch.cuda.is_available():
    torch.cuda.manual_seed_all(SEED)

# -----------------------------
# Step 1: Load and preprocess data
# -----------------------------
with open("balanced_labeled_conversations.json", "r") as f:
    data = json.load(f)

df = pd.DataFrame(data)
traits = ["witty", "sarcastic", "charming"]
df = df[df['trait'].isin(traits)].dropna(subset=["dialogue"])

def format_conversation_with_expert_token(row):
    expert_token = f"[EXPERT: {row['trait'].upper()}]"
    return f"{expert_token} <|user|> {row['dialogue']}\n<|assistant|>"

df["text"] = df.apply(format_conversation_with_expert_token, axis=1)
df = df.sample(frac=0.1, random_state=SEED).reset_index(drop=True)
print(f"Using {len(df)} examples ({len(df)/len(data):.0%} of original)")

# -----------------------------
# Step 2: Tokenization and special token addition
# -----------------------------
model_checkpoint = "deepseek-ai/deepseek-moe-16b-base"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, trust_remote_code=True)

# Add expert control tokens explicitly
special_tokens = [f"[EXPERT: {trait.upper()}]" for trait in traits]
num_added = tokenizer.add_tokens(special_tokens)
print(f"Added {num_added} special tokens: {special_tokens}")

# Ensure pad_token is defined
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

def tokenize_data(batch):
    return tokenizer(
        batch["text"],
        padding="max_length",
        truncation=True,
        max_length=512,
    )

# Build Dataset from just the sampled df
dataset = Dataset.from_pandas(df[["text"]])
tokenized_dataset = dataset.map(tokenize_data, batched=True, remove_columns=["text"])

# -----------------------------
# Step 3: Load model and resize embeddings
# -----------------------------

# Configure 8-bit quantization
bnb_config = BitsAndBytesConfig(
    load_in_8bit=True,
    bnb_8bit_compute_dtype=torch.bfloat16,
    bnb_8bit_quant_type="nf4",
    bnb_8bit_use_double_quant=True,
)


model = AutoModelForCausalLM.from_pretrained(
    model_checkpoint,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config, # Use quantization_config instead of load_in_8bit
)

model.resize_token_embeddings(len(tokenizer))

# Prepare model for 8-bit training and add LoRA adapters
model = prepare_model_for_kbit_training(model)

lora_config = LoraConfig(
    r=16, # LoRA attention dimension
    lora_alpha=32, # Alpha parameter for LoRA scaling
    target_modules=["q_proj", "v_proj"], # Modules to apply LoRA to
    lora_dropout=0.05, # Dropout probability for LoRA layers
    bias="none", # Bias type
    task_type="CAUSAL_LM", # Task type
)

model = get_peft_model(model, lora_config)
model.print_trainable_parameters()


# -----------------------------
# Step 4: Training setup
# -----------------------------

output_dir = "./deepseek_moe_expert_prompting"

training_args = TrainingArguments(
    output_dir=output_dir,
    per_device_train_batch_size=1, # Reduced batch size
    gradient_accumulation_steps=8, # Increased gradient accumulation steps
    num_train_epochs=1,
    save_steps=500,
    logging_dir="./logs",
    logging_steps=50,
    do_eval=False,
    report_to="none",
    seed=SEED,
    # Add checkpointing arguments
    save_strategy="steps",
    save_total_limit=2, # Keep up to 2 checkpoints
    load_best_model_at_end=False, # We are not doing evaluation
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset,
    tokenizer=tokenizer,
    data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),
)

print("Starting training...")

# Check if a checkpoint exists to resume from
latest_checkpoint = None
if os.path.exists(output_dir):
    # This assumes checkpoint directories are named like "checkpoint-XXX"
    checkpoint_dirs = [d for d in os.listdir(output_dir) if os.path.isdir(os.path.join(output_dir, d)) and d.startswith("checkpoint-")]
    if checkpoint_dirs:
        # Find the latest checkpoint based on step number
        latest_checkpoint_dir = max(checkpoint_dirs, key=lambda d: int(d.split("-")[1]))
        latest_checkpoint = os.path.join(output_dir, latest_checkpoint_dir)
        print(f"Resuming from checkpoint: {latest_checkpoint}")

# Use resume_from_checkpoint if a checkpoint is found
trainer.train(resume_from_checkpoint=latest_checkpoint)

print("Training complete.")

trainer.save_model(output_dir)
tokenizer.save_pretrained(output_dir)
```
config.json: 
 1.07k/? [00:00<00:00, 104kB/s]
configuration_deepseek.py: 
 10.2k/? [00:00<00:00, 1.23MB/s]
A new version of the following files was downloaded from https://huggingface.co/deepseek-ai/deepseek-moe-16b-base:
- configuration_deepseek.py
. Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
modeling_deepseek.py: 
 72.7k/? [00:00<00:00, 8.84MB/s]
A new version of the following files was downloaded from https://huggingface.co/deepseek-ai/deepseek-moe-16b-base:
- modeling_deepseek.py
. Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
model.safetensors.index.json: 
 490k/? [00:00<00:00, 27.5MB/s]
Fetching 7 files:   0%
 0/7 [00:10<?, ?it/s]
model-00001-of-00007.safetensors:  22%
 1.07G/5.00G [00:10<00:15, 245MB/s]
model-00005-of-00007.safetensors:  21%
 1.07G/5.00G [00:10<00:33, 116MB/s]
model-00006-of-00007.safetensors:  11%
 539M/5.00G [00:10<00:25, 178MB/s]
model-00004-of-00007.safetensors:  17%
 873M/5.00G [00:09<00:24, 169MB/s]
model-00007-of-00007.safetensors:  53%
 1.48G/2.77G [00:09<00:05, 223MB/s]
model-00002-of-00007.safetensors:   0%
 747k/5.00G [00:10<10:41:29, 130kB/s]
model-00003-of-00007.safetensors:  16%
 807M/5.00G [00:10<00:23, 175MB/s]
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In[4], line 14
      1 # -----------------------------
      2 # Step 3: Load model and resize embeddings
      3 # -----------------------------
      4 
      5 # Configure 8-bit quantization
      6 bnb_config = BitsAndBytesConfig(
      7     load_in_8bit=True,
      8     bnb_8bit_compute_dtype=torch.bfloat16,
      9     bnb_8bit_quant_type="nf4",
     10     bnb_8bit_use_double_quant=True,
     11 )
---> 14 model = AutoModelForCausalLM.from_pretrained(
     15     model_checkpoint,
     16     trust_remote_code=True,
     17     torch_dtype=torch.bfloat16,
     18     device_map="auto",
     19     quantization_config=bnb_config, # Use quantization_config instead of load_in_8bit
     20 )
     22 model.resize_token_embeddings(len(tokenizer))
     24 # Prepare model for 8-bit training and add LoRA adapters

File /usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py:593, in _BaseAutoModelClass.from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
    591         model_class.register_for_auto_class(auto_class=cls)
    592     model_class = add_generation_mixin_to_remote_model(model_class)
--> 593     return model_class.from_pretrained(
    594         pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs
    595     )
    596 elif type(config) in cls._model_mapping.keys():
    597     model_class = _get_model_class(config, cls._model_mapping)

File /usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:311, in restore_default_torch_dtype.<locals>._wrapper(*args, **kwargs)
    309 old_dtype = torch.get_default_dtype()
    310 try:
--> 311     return func(*args, **kwargs)
    312 finally:
    313     torch.set_default_dtype(old_dtype)

File /usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:4680, in PreTrainedModel.from_pretrained(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, weights_only, *model_args, **kwargs)
   4670 if (
   4671     gguf_file
   4672     and device_map is not None
   4673     and ((isinstance(device_map, dict) and "disk" in device_map.values()) or "disk" in device_map)
   4674 ):
   4675     raise RuntimeError(
   4676         "One or more modules is configured to be mapped to disk. Disk offload is not supported for models "
   4677         "loaded from GGUF files."
   4678     )
-> 4680 checkpoint_files, sharded_metadata = _get_resolved_checkpoint_files(
   4681     pretrained_model_name_or_path=pretrained_model_name_or_path,
   4682     subfolder=subfolder,
   4683     variant=variant,
   4684     gguf_file=gguf_file,
   4685     from_tf=from_tf,
   4686     from_flax=from_flax,
   4687     use_safetensors=use_safetensors,
   4688     cache_dir=cache_dir,
   4689     force_download=force_download,
   4690     proxies=proxies,
   4691     local_files_only=local_files_only,
   4692     token=token,
   4693     user_agent=user_agent,
   4694     revision=revision,
   4695     commit_hash=commit_hash,
   4696     is_remote_code=cls._auto_class is not None,
   4697     transformers_explicit_filename=transformers_explicit_filename,
   4698 )
   4700 is_sharded = sharded_metadata is not None
   4701 is_quantized = hf_quantizer is not None

File /usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:1295, in _get_resolved_checkpoint_files(pretrained_model_name_or_path, subfolder, variant, gguf_file, from_tf, from_flax, use_safetensors, cache_dir, force_download, proxies, local_files_only, token, user_agent, revision, commit_hash, is_remote_code, transformers_explicit_filename)
   1293 sharded_metadata = None
   1294 if is_sharded:
-> 1295     checkpoint_files, sharded_metadata = get_checkpoint_shard_files(
   1296         pretrained_model_name_or_path,
   1297         resolved_archive_file,
   1298         cache_dir=cache_dir,
   1299         force_download=force_download,
   1300         proxies=proxies,
   1301         local_files_only=local_files_only,
   1302         token=token,
   1303         user_agent=user_agent,
   1304         revision=revision,
   1305         subfolder=subfolder,
   1306         _commit_hash=commit_hash,
   1307     )
   1308 else:
   1309     checkpoint_files = [resolved_archive_file] if pretrained_model_name_or_path is not None else None

File /usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py:1110, in get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, cache_dir, force_download, proxies, resume_download, local_files_only, token, user_agent, revision, subfolder, _commit_hash, **deprecated_kwargs)
   1106     return shard_filenames, sharded_metadata
   1108 # At this stage pretrained_model_name_or_path is a model identifier on the Hub. Try to get everything from cache,
   1109 # or download the files
-> 1110 cached_filenames = cached_files(
   1111     pretrained_model_name_or_path,
   1112     shard_filenames,
   1113     cache_dir=cache_dir,
   1114     force_download=force_download,
   1115     proxies=proxies,
   1116     resume_download=resume_download,
   1117     local_files_only=local_files_only,
   1118     token=token,
   1119     user_agent=user_agent,
   1120     revision=revision,
   1121     subfolder=subfolder,
   1122     _commit_hash=_commit_hash,
   1123 )
   1125 return cached_filenames, sharded_metadata

File /usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py:557, in cached_files(path_or_repo_id, filenames, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)
    554     # Any other Exception type should now be re-raised, in order to provide helpful error messages and break the execution flow
    555     # (EntryNotFoundError will be treated outside this block and correctly re-raised if needed)
    556     elif not isinstance(e, EntryNotFoundError):
--> 557         raise e
    559 resolved_files = [
    560     _get_cache_file_to_return(path_or_repo_id, filename, cache_dir, revision) for filename in full_filenames
    561 ]
    562 # If there are any missing file and the flag is active, raise

File /usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py:485, in cached_files(path_or_repo_id, filenames, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)
    470         hf_hub_download(
    471             path_or_repo_id,
    472             filenames[0],
   (...)
    482             local_files_only=local_files_only,
    483         )
    484     else:
--> 485         snapshot_download(
    486             path_or_repo_id,
    487             allow_patterns=full_filenames,
    488             repo_type=repo_type,
    489             revision=revision,
    490             cache_dir=cache_dir,
    491             user_agent=user_agent,
    492             force_download=force_download,
    493             proxies=proxies,
    494             resume_download=resume_download,
    495             token=token,
    496             local_files_only=local_files_only,
    497         )
    499 except Exception as e:
    500     # We cannot recover from them
    501     if isinstance(e, RepositoryNotFoundError) and not isinstance(e, GatedRepoError):

File /usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py:114, in validate_hf_hub_args.<locals>._inner_fn(*args, **kwargs)
    111 if check_use_auth_token:
    112     kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs)
--> 114 return fn(*args, **kwargs)

File /usr/local/lib/python3.10/dist-packages/huggingface_hub/_snapshot_download.py:327, in snapshot_download(repo_id, repo_type, revision, cache_dir, local_dir, library_name, library_version, user_agent, proxies, etag_timeout, force_download, token, local_files_only, allow_patterns, ignore_patterns, max_workers, tqdm_class, headers, endpoint, local_dir_use_symlinks, resume_download)
    325         _inner_hf_hub_download(file)
    326 else:
--> 327     thread_map(
    328         _inner_hf_hub_download,
    329         filtered_repo_files,
    330         desc=tqdm_desc,
    331         max_workers=max_workers,
    332         # User can use its own tqdm class or the default one from `huggingface_hub.utils`
    333         tqdm_class=tqdm_class or hf_tqdm,
    334     )
    336 if local_dir is not None:
    337     return str(os.path.realpath(local_dir))

File /usr/local/lib/python3.10/dist-packages/tqdm/contrib/concurrent.py:69, in thread_map(fn, *iterables, **tqdm_kwargs)
     55 """
     56 Equivalent of `list(map(fn, *iterables))`
     57 driven by `concurrent.futures.ThreadPoolExecutor`.
   (...)
     66     [default: max(32, cpu_count() + 4)].
     67 """
     68 from concurrent.futures import ThreadPoolExecutor
---> 69 return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)

File /usr/local/lib/python3.10/dist-packages/tqdm/contrib/concurrent.py:51, in _executor_map(PoolExecutor, fn, *iterables, **tqdm_kwargs)
     47 with ensure_lock(tqdm_class, lock_name=lock_name) as lk:
     48     # share lock in case workers are already using `tqdm`
     49     with PoolExecutor(max_workers=max_workers, initializer=tqdm_class.set_lock,
     50                       initargs=(lk,)) as ex:
---> 51         return list(tqdm_class(ex.map(fn, *iterables, chunksize=chunksize), **kwargs))

File /usr/local/lib/python3.10/dist-packages/tqdm/notebook.py:250, in tqdm_notebook.__iter__(self)
    248 try:
    249     it = super().__iter__()
--> 250     for obj in it:
    251         # return super(tqdm...) will not catch exception
    252         yield obj
    253 # NB: except ... [ as ...] breaks IPython async KeyboardInterrupt

File /usr/local/lib/python3.10/dist-packages/tqdm/std.py:1181, in tqdm.__iter__(self)
   1178 time = self._time
   1180 try:
-> 1181     for obj in iterable:
   1182         yield obj
   1183         # Update and possibly print the progressbar.
   1184         # Note: does not call self.update(1) for speed optimisation.

File /usr/lib/python3.10/concurrent/futures/_base.py:621, in Executor.map.<locals>.result_iterator()
    618 while fs:
    619     # Careful not to keep a reference to the popped future
    620     if timeout is None:
--> 621         yield _result_or_cancel(fs.pop())
    622     else:
    623         yield _result_or_cancel(fs.pop(), end_time - time.monotonic())

File /usr/lib/python3.10/concurrent/futures/_base.py:319, in _result_or_cancel(***failed resolving arguments***)
    317 try:
    318     try:
--> 319         return fut.result(timeout)
    320     finally:
    321         fut.cancel()

File /usr/lib/python3.10/concurrent/futures/_base.py:458, in Future.result(self, timeout)
    456     raise CancelledError()
    457 elif self._state == FINISHED:
--> 458     return self.__get_result()
    459 else:
    460     raise TimeoutError()

File /usr/lib/python3.10/concurrent/futures/_base.py:403, in Future.__get_result(self)
    401 if self._exception:
    402     try:
--> 403         raise self._exception
    404     finally:
    405         # Break a reference cycle with the exception in self._exception
    406         self = None

File /usr/lib/python3.10/concurrent/futures/thread.py:58, in _WorkItem.run(self)
     55     return
     57 try:
---> 58     result = self.fn(*self.args, **self.kwargs)
     59 except BaseException as exc:
     60     self.future.set_exception(exc)

File /usr/local/lib/python3.10/dist-packages/huggingface_hub/_snapshot_download.py:301, in snapshot_download.<locals>._inner_hf_hub_download(repo_file)
    300 def _inner_hf_hub_download(repo_file: str):
--> 301     return hf_hub_download(
    302         repo_id,
    303         filename=repo_file,
    304         repo_type=repo_type,
    305         revision=commit_hash,
    306         endpoint=endpoint,
    307         cache_dir=cache_dir,
    308         local_dir=local_dir,
    309         local_dir_use_symlinks=local_dir_use_symlinks,
    310         library_name=library_name,
    311         library_version=library_version,
    312         user_agent=user_agent,
    313         proxies=proxies,
    314         etag_timeout=etag_timeout,
    315         resume_download=resume_download,
    316         force_download=force_download,
    317         token=token,
    318         headers=headers,
    319     )

File /usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py:114, in validate_hf_hub_args.<locals>._inner_fn(*args, **kwargs)
    111 if check_use_auth_token:
    112     kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs)
--> 114 return fn(*args, **kwargs)

File /usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1008, in hf_hub_download(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, user_agent, force_download, proxies, etag_timeout, token, local_files_only, headers, endpoint, resume_download, force_filename, local_dir_use_symlinks)
    988     return _hf_hub_download_to_local_dir(
    989         # Destination
    990         local_dir=local_dir,
   (...)
   1005         local_files_only=local_files_only,
   1006     )
   1007 else:
-> 1008     return _hf_hub_download_to_cache_dir(
   1009         # Destination
   1010         cache_dir=cache_dir,
   1011         # File info
   1012         repo_id=repo_id,
   1013         filename=filename,
   1014         repo_type=repo_type,
   1015         revision=revision,
   1016         # HTTP info
   1017         endpoint=endpoint,
   1018         etag_timeout=etag_timeout,
   1019         headers=hf_headers,
   1020         proxies=proxies,
   1021         token=token,
   1022         # Additional options
   1023         local_files_only=local_files_only,
   1024         force_download=force_download,
   1025     )

File /usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1161, in _hf_hub_download_to_cache_dir(cache_dir, repo_id, filename, repo_type, revision, endpoint, etag_timeout, headers, proxies, token, local_files_only, force_download)
   1158 # Local file doesn't exist or etag isn't a match => retrieve file from remote (or cache)
   1160 with WeakFileLock(lock_path):
-> 1161     _download_to_tmp_and_move(
   1162         incomplete_path=Path(blob_path + ".incomplete"),
   1163         destination_path=Path(blob_path),
   1164         url_to_download=url_to_download,
   1165         proxies=proxies,
   1166         headers=headers,
   1167         expected_size=expected_size,
   1168         filename=filename,
   1169         force_download=force_download,
   1170         etag=etag,
   1171         xet_file_data=xet_file_data,
   1172     )
   1173     if not os.path.exists(pointer_path):
   1174         _create_symlink(blob_path, pointer_path, new_blob=True)

File /usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1710, in _download_to_tmp_and_move(incomplete_path, destination_path, url_to_download, proxies, headers, expected_size, filename, force_download, etag, xet_file_data)
   1708 if xet_file_data is not None and is_xet_available():
   1709     logger.debug("Xet Storage is enabled for this repo. Downloading file from Xet Storage..")
-> 1710     xet_get(
   1711         incomplete_path=incomplete_path,
   1712         xet_file_data=xet_file_data,
   1713         headers=headers,
   1714         expected_size=expected_size,
   1715         displayed_filename=filename,
   1716     )
   1717 else:
   1718     if xet_file_data is not None:

File /usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:627, in xet_get(incomplete_path, xet_file_data, headers, expected_size, displayed_filename, _tqdm_bar)
    624 def progress_updater(progress_bytes: float):
    625     progress.update(progress_bytes)
--> 627 download_files(
    628     xet_download_info,
    629     endpoint=connection_info.endpoint,
    630     token_info=(connection_info.access_token, connection_info.expiration_unix_epoch),
    631     token_refresher=token_refresher,
    632     progress_updater=[progress_updater],
    633 )

RuntimeError: Data processing error: CAS service error : IO Error: No space left on device (os error 28)

Additionally, I keep getting this popup:
File Save Error for deepseek-moe-16b.ipynb
Unexpected error while saving file: workspace/deepseek-moe-16b.ipynb unable to open database file

Context: This code was made for a colab notebook. Its now being run on jupyter notebook via runpod.

The key error here is:

```
RuntimeError: Data processing error: CAS service error : IO Error: No space left on device (os error 28)
```

This means your disk or the available storage space on the device (or within the container) has run out of space. The model files you are downloading (deepseek-moe-16b-base) are very large (~16B parameters, multi-GB safetensors shards), which can easily exceed the free disk space available on the system.

---

### Steps to fix this:

#### 1. **Free up disk space**
- Delete any unnecessary files on your machine or environment.
- Clean cache folders, e.g.,
  - Hugging Face cache folder is usually at `~/.cache/huggingface` or similar.
  - PyTorch cache: `~/.cache/torch`
- In your Jupyter environment, try running shell commands to check space:

```bash
!df -h
!du -sh ~/.cache/huggingface
!rm -rf ~/.cache/huggingface
```

- If your environment provides persistent storage outside the container (like RunPod volumes), make sure the cache and model downloads are done there (move cache with environment variables).

#### 2. **Use `local_files_only=True` if the model is already cached**

If you've partially downloaded the model and want to avoid downloading again, but you don't have the files locally because the download failed, this might cause issues. So ensure either:

- The model is already fully cached.
- Or `local_files_only=False` to re-attempt download.

#### 3. **Specify cache directory on a disk with sufficient space**

You can set the `TRANSFORMERS_CACHE` and `HF_HOME` environment variables before running your script to point to a directory with more free space:

```python
import os
os.environ["TRANSFORMERS_CACHE"] = "/path/to/bigger/storage/huggingface_cache"
os.environ["HF_HOME"] = "/path/to/bigger/storage/huggingface_cache"
```

Make sure this path has enough free space.

---

### About the Jupyter "File Save Error"
```
Unexpected error while saving file: workspace/deepseek-moe-16b.ipynb unable to open database file
```

This is also a symptom of the underlying problem:

- Jupyter or your notebook server cannot write to disk because:
  - No disk space left.
  - Permissions error.

Check that:

- The workspace directory has write permissions.
- There's enough disk space.

---

### Additional recommendations

- As you are using a very large model (`deepseek-moe-16b-base`), for smaller test runs or debugging, try a smaller checkpoint to ensure code correctness.
- Run your notebook on a system/storage with sufficient disk. Models this big require dozens of GB free disk space just for downloading.
- Consider using streaming loading options for datasets or sharded loading to reduce disk I/O if supported.
- Occasionally restart your environment to clear temp files that might be taking space.

---

### Summary

- **Main problem:** No disk space left to download/store the model shards.
- **Fix:** Free disk space or change cache location to bigger disk.
- **Check:** disk free space with `df -h`.
- **Fix Jupyter save errors:** Ensuring disk space + write permissions.

---

If you want concrete commands to check disk usage or clean cache, or help setting environment variables for cache, just ask!

你好

你好！有什么我可以帮你的吗？

```
<?php
declare(strict_types=1);

namespace app\components;

// PHP built-in exception:
use InvalidArgumentException;

use Yii;
use yii\base\Model;
use yii\web\Request;

use app\components\traits\CacheJsCssTrait;
use app\helpers\LogHelper;
use app\helpers\ParamHelper;
use app\helpers\TimeHelper;

/**
* Honeypot for Controller class 
*
* Controller usage example:
* 
* 	public function actionContact()
* 	{
* 		$model = new \app\models\ContactForm();
* 		
*		// (Optional) You can define customized pools per model if you want,
*		// otherwise the manager falls back to default pool
* 		$honeypotPools = [
* 			\app\models\ContactForm::class => ['firstname', 'lastname', 'username', 'email', 'phone', 
*				'address', 'company', 'website', 'city', 'zip', 'comment'],
*			
* 			// You can add others models here
* 		];
* 		
*		// Instantiate your HoneypotManager passing your customized pools, min, max traps
* 		$honeypotManager = new \app\components\HoneypotManager($honeypotPools, 2, 5);
*
*		// Prepare traps: select traps to display and fatal traps randomly
* 		$hpData = $honeypotManager->prepareHoneypots($model);
*		$hpFields = $hpData['displayed'];
*		$hpFatalFields = $hpData['fatal'];
* 		
* 		$request = \Yii::$app->request;
*		
*		// Load POST data, log filled honeypots, and validate the model; returns true if valid
*		if ($honeypotManager->handleRequest($model, $hpFields))
* 		{
* 			if ($model->process())
* 			{
* 				\Yii::$app->session->setFlash('success', 'Thanks for contacting us.');
* 				return $this->refresh();
* 			}
*			
*			// Check if any fatal honeypot trap was triggered
*			if ($model->isFatalHoneypotFilled())
*			{
*				 // Example: mark the model’s status as spam and save without further validation
*				$model->status = 'spam';
*				$model->save(false);
*			}
* 			
* 			\Yii::$app->session->setFlash('error', 'Failed to process form.');
* 		}
* 		
* 		return $this->render('contact', [
* 			'model' => $model,
*			// Honeypot variables below:
* 			'hpFields' => $hpFields,// Honeypot - For the view: which fields to render hidden
* 			'hpFatalFields' => $hpFatalFields,// Honeypot - For logging or debugging. Optional
* 		]);
* 	}
*
* View useage example:
* 
* 	$css = <<<CSS
* 	.hp-hidden,
* 	.hp-hidden input,
* 	.hp-hidden textarea
* 	{
* 		position: absolute !important;
* 		left: -9999px !important;
* 		top: -9999px !important;
* 		height: 1px !important;
* 		width: 1px !important;
* 		overflow: hidden !important;
* 		pointer-events: none !important;
* 	}
* 	CSS;
* 	
* 	$this->registerCss($css);
* 	?>
* 	
* 	<?php
* 	// Render honeypot fields hidden
*	$placeholders = $model::honeypotPlaceholders();
*	
* 	foreach ($hpFields as $field) :
*		$placeholder = $placeholders[$field] ?? "Enter " . ucfirst($field);
*		$label = $labels[$field] ?? ucfirst($field);
* 	?>
* 	<?= $form->field($model, $field, ['options' => ['class' => 'hp-hidden']])
* 		->textInput([
*			'tabindex' => '-1',
*			'autocomplete' => 'off',
*			'placeholder' => $placeholder,
*			'class' => 'form-control square'
* 		])->label($label);
* 	?>
* 	<?php endforeach; ?>
* 	
* 	<?php
* 	// For debugging only, includes fatal honeypot names in HTML comments
* 	if (!empty($hpFatalFields)) : 
* 	?>
* 	<?= implode(', ', $hpFatalFields); ?>
* 	<?php endif; ?>
*/
class HoneypotManager
{
	private bool $_debugMode = true;
	
	/**
	* @var array mapping model class names => pool of honeypot fields
	* If model class not listed here, fallback to defaultPool
	*/
	protected array $__modelPools = [];
	
	/**
	* @var string[] Default honeypot trap names used if no model-specific pool found
	*/
	protected array $__defaultPool = ['firstname', 'lastname', 'username', 'email', 'phone', 'address', 'company', 'website', 'city', 'zip', 'comment'];
	
	protected int $__minCount;
	protected int $__maxCount;
	
	// Note: Every protected variable below CacheJsCssTrait is used by the trait
	use CacheJsCssTrait;
	
	protected bool $__cacheDebugMode = false;
	protected bool $__compressJsCss = false;
	protected bool $__cacheEnable = false;
	protected int $__cacheJsCssTime = 3600;// +1 hour
	protected string $__cacheKeyPrefix = 'honeypot_manager_css';
	
	const DEFAULT_DURATION_SECONDS = 3600;// +1 hour
	
	/**
	* Construct
	* @param array $modelPools Optional per-model trap pools
	* @param int $minCount min traps per form
	* @param int $maxCount max traps per form
	* @param array|null $defaultPool Global default pool
	*/
	public function __construct(
		array $modelPools = [],
		int $minCount = 2,
		int $maxCount = 7,
		?array $defaultPool = null
	)
	{
		$this->__modelPools = $modelPools;
		
		$this->__minCount = $minCount;
		$this->__maxCount = $maxCount;
		$this->__defaultPool = !empty($defaultPool) ? $defaultPool 
			: (!empty($modelPools) ? $modelPools : $this->__defaultPool);
		
		// Get cacheDuration from Param and set with TimeHelper
		$cacheDuration = ParamHelper::get('widgets.cacheDuration');
		$this->__cacheJsCssTime = TimeHelper::convertDurationToSeconds($cacheDuration);
		
		// Cache & Compression
		$this->__cacheEnable = ParamHelper::get('widgets.cacheEnable');
		$this->__compressJsCss = ParamHelper::get('widgets.compressJsCss');
	}
	
	/**
	* Prepares honeypots for the given model.
	* @param Model $model Model using HoneypotTrait with setHoneypots()/setFatalHoneypots()
	* @return array ['displayed' => array, 'fatal' => array]
	* @throws \InvalidArgumentException
	*/
	public function prepareHoneypots(Model $model): array
	{
		if (!method_exists($model, 'setHoneypots')
			|| !method_exists($model, 'setFatalHoneypots')
		)
		{
			throw new InvalidArgumentException(sprintf(
				'Model %s must implement setHoneypots() and setFatalHoneypots()',
				get_class($model)
			));
		}
		
		$modelClass = get_class($model);
		
		$pool = $this->__modelPools[$modelClass]
			?? (method_exists($modelClass, 'honeypotPool') ? $modelClass::honeypotPool() 
			: $this->__defaultPool);
		
		$count = random_int($this->__minCount, min($this->__maxCount, count($pool)));
		
		$selected = (array) array_rand(array_flip($pool), $count);
		
		if (is_string($selected))
		{
			$selected = [$selected];
		}
		
		$fatalCount = random_int(1, (int) max(1, floor(count($selected) / 2)));
		$fatal = (array) array_rand(array_flip($selected), $fatalCount);
			
		if (is_string($fatal))
		{
			$fatal = [$fatal];
		}
		
		if ($this->_debugMode)
		{
			LogHelper::debug('Honeypot Manager - Prepare',
				[
					'model' => $modelClass,
					'pool' => $pool,
					'selected' => $selected,
					'fatal' => $fatal,
					'method' => __METHOD__
				]
			);
		}
		
		$model->setHoneypots($selected);
		$model->setFatalHoneypots($fatal);
		
		return ['displayed' => $selected, 'fatal' => $fatal];
	}
	
	/**
	* Handle Javascript and Caching
	*/
	protected function registerCss(): void
	{
		$cacheKey = $this->__cacheKeyPrefix . $widgetId . '_css_block_';
		
		$cssBlock = $this->getCachedCssBlock($cacheKey, function ()
		{
			return <<<CSS
			CSS;
		});
		
		// Register the JavaScript.
		$this->getView()->registerCss($cssBlock);
	}
	
	/**
	* Loads POST data into model, logs filled honeypots, and validates the model
	* @param Model $model Form model with HoneypotTrait
	* @param Request|null $request Optional request object, defaults to Yii::$app->request
	* @param array $honeypots The honeypot fields assigned (normally from prepareHoneypots)
	* @return bool True if load & validate succeeded, false otherwise
	*/
	public function handleRequest(Model $model, array $honeypots, ?Request $request = null): bool
	{
		$request = $request ?? \Yii::$app->request;
		
		if ($request->isPost && $model->load($request->post()))
		{
			$filled = [];
			
			foreach ($honeypots as $field)
			{
				$val = $model->$field ?? null;
				
				if ($val !== null && $val !== '')
				{
					$filled[$field] = $val;
				}
			}
			
			if ($this->_debugMode)
			{
				LogHelper::debug('Honeypot Manager - Handle request',
					[
						'filledHoneypots' => $filled,
						'ip' => $request->userIP,
						'userAgent' => $request->userAgent,
						'method' => __METHOD__
					]
				);
			}
			
			return $model->validate();
		}
		
		return false;
	}
}

<?php
declare(strict_types=1);

namespace app\components\traits;

use yii\base\Model;

use app\helpers\LogHelper;
use app\helpers\RoleHelper;

/**
* Trait HoneypotTrait
* Provides reusable honeypot anti-spam protection for Yii2 Models.
*
* HOW TO USE:
*
* 1) Define in your Model:
*    - Public properties for your form fields *and* their honeypot variants.
*      Example:
*          public string $name = '';
*          public string $namee = ''; // honeypot variant for 'name'
*
*    - A property called $honeypotPairs, mapping logical field names to a pair of attributes:
*          [
*              'name' => ['name', 'namee'],
*              'email' => ['email', 'emaill'],
*              'subject' => ['subject', 'subjectt'],
*              // do NOT include large fields like 'body' if you want to exclude from honeypots
*          ]
*
* 2) Add HoneypotTrait to your Model class:
*      use \app\components\HoneypotTrait;
*
* 3) In your model's validation rules(), include:
*      - Required validation only for the *active* (non-honeypot) field.
*      - Call `validateHoneypotEmpty` on honeypot fields to ensure they remain empty.
*
* 4) In your controller action handling the form:
*      - Instantiate your model.
*      - Generate honeypot assignments by randomly selecting which version in each pair is the honeypot.
*      - Store the list of honeypot fields in the model using setHoneypots().
*      - Pass honeypot info to your view for rendering.
*      - On POST, load and validate model normally.
*      - Honeypot validation will trigger errors if bots fill the honeypot fields.
*
* 5) In your form view:
*      - Render all form fields *and* their honeypot counterparts.
*      - Visibly show only the *real* (active) fields.
*      - Hide honeypot fields with CSS (e.g., visually off-screen) so human users don’t see them.
*
* WHY THIS WORKS:
*
* - Bots that autofill all form inputs will fill the honeypot fields, triggering validation errors.
* - Real users never fill honeypots (because they are hidden).
* - Randomizing honeypot selection per request or session prevents bots from learning which fields to avoid.
* - Mapping logical fields to pairs lets your validator know which attributes to require vs which are honeypots.
*
* IMPORTANT NOTES:
*
* - Do NOT randomize the real form attribute *names* themselves — keep consistent names for correct Yii2 model loading.
* - Define honeypot variants that closely resemble real fields to avoid bot detection by unusual names.
* - Usually restrict honeypots to basic inputs like 'name', 'email', 'subject' — exclude large text areas like 'body'.
*
* How to use in model/form:
* 
* 	namespace app\models;
* 	
* 	use yii\base\Model;
* 	use app\components\HoneypotTrait;
* 	
* 	class ContactForm extends Model
* 	{
* 		use HoneypotTrait;
* 		
* 		public string $name = '';
* 		public string $email = '';
* 		public string $subject = '';
* 		public string $body = '';
* 		
* 		public function rules()
* 		{
* 			return array_merge([
* 				[['name', 'email', 'subject', 'body'], 'required'],
* 				['email', 'email'],
* 			], $this->honeypotRules());
* 		}
* 		
* 		// Placeholder for actual processing logic (sending email, saving...)
* 		public function process(): bool
* 		{
* 			return true;
* 		}
* 	}
*/
trait HoneypotTrait
{
	private bool $_debugMode = true;

	// Declare honeypot trap fields as public properties
	public string $firstname = '';
	public string $lastname = '';
	public string $username = '';
	public string $email = '';
	public string $phone = '';
	public string $address = '';
	public string $company = '';
	public string $website = '';
	public string $city = '';
	public string $zip = '';
	public string $comment = '';
	
	/**
	* Returns the full pool of honeypot field names.
	* Override to customize per model.
	*/
	public static function honeypotPool(): array
	{
		return ['firstname', 'lastname', 'username', 'email', 'phone', 'address', 'company', 'website', 'city', 'zip', 'comment'];
	}
	
	/**
	* Returns user-friendly placeholders matching the honeypot fields.
	* Override this method if you want to customize placeholders per model.
	*/
	public static function honeypotPlaceholders(): array
	{
		return [
			'firstname' => \Yii::t('app', 'Enter first name'),
			'lastname' => \Yii::t('app', 'Enter last name'),
			'username' => \Yii::t('app', 'Enter username'),
			'email' => \Yii::t('app', 'Enter email'),
			'phone' => \Yii::t('app', 'Enter phone number'),
			'address' => \Yii::t('app', 'Enter address'),
			'company' => \Yii::t('app', 'Enter company name'),
			'website' => \Yii::t('app', 'Enter website URL'),
			'city' => \Yii::t('app', 'Enter city'),
			'zip' => \Yii::t('app', 'Enter ZIP/postal code'),
			'comment' => \Yii::t('app', 'Enter comment'),
		];
	}
	
	/**
	* Array of currently assigned honeypot fields for this request/session.
	* @var string[]
	*/
	protected array $__honeypots = [];
	
	/**
	* Array of currently assigned *fatal* honeypot fields.
	* Filling one of these causes immediate validation failure.
	* @var string[]
	*/
	protected array $__fatalHoneypots = [];
	
	/**
	* Assigns the honeypot fields for the current request or user session.
	* @param string[] $fields Array of attribute names assigned as honeypots.
	*
	* Typically called by controller after randomizing honeypot selection.
	*/
	public function setHoneypots(array $fields): void
	{
		$this->__honeypots = $fields;
		
		if ($this->_debugMode)
		{
			LogHelper::debug('HoneypotTrait - Set honeypots', ['fields' => $fields]);
		}
	}
	
	/**
	* Returns the currently assigned honeypot fields.
	* @return string[]
	*/
	public function getHoneypots(): array
	{
		return $this->__honeypots;
	}
	
	/**
	* Assign fatal honeypot fields for this request/session.
	* @param string[] $fields Array of attribute names causing immediate validation failure if filled.
	*/
	public function setFatalHoneypots(array $fields): void
	{
		$this->__fatalHoneypots = $fields;
		
		if ($this->_debugMode)
		{
			LogHelper::debug('HoneypotTrait - Set fatal honeypots', ['fields' => $fields]);
		}
	}
	
	/**
	* Returns currently assigned fatal honeypot fields.
	* @return string[]
	*/
	public function getFatalHoneypots(): array
	{
		return $this->__fatalHoneypots;
	}
	
	/**
	* Checks if any of the fatal honeypot fields have been filled
	* @return bool True if any fatal honeypot field is filled, false otherwise.
	*
	* This method inspects the current state of the model and returns true
	* if any of the honeypot traps classified as "fatal" contain values.
	*/
	public function isFatalHoneypotFilled(): bool
	{
		foreach ($this->getFatalHoneypots() as $fatalField)
		{
			if (!empty($this->$fatalField))
			{
				return true;
			}
		}
		
		return false;
	}
	
	/**
	* Returns validation rules for honeypots: safe + custom validator
	*/
	public function honeypotRules(): array
	{
		$pool = static::honeypotPool();
		
		return [
			[$pool, 'safe'],
			[$pool, 'validateHoneypots'],
		];
	}
	
	/**
	* Validator: fail if any of the active honeypots has a value.
	* @param string $attribute
	* @param array|null $params
	*/
	public function validateHoneypots(string $attribute, $params): void
	{
		// not an active honeypot
		if (!in_array($attribute, $this->__honeypots, true))
		{
			return;
		}
		
		// honeypot not filled
		if (empty($this->$attribute))
		{
			return;
		}
		
		$userIp = RoleHelper::userIp();
		$userAgent = RoleHelper::userAgent();
		
		if (in_array($attribute, $this->__fatalHoneypots, true))
		{
			LogHelper::logBotAction("Fatal honeypot trap '{$attribute}' filled - bot detected", [
				'ip' => $userIp,
				'userAgent' => $userAgent,
				'attribute' => $attribute,
				'method' => __METHOD__
			]);
			
			// Add generic error to block submission silently
			//$this->addError('_honeypots', 'Unable to process your submission.');
		}
		else
		{
			LogHelper::logBotAction("Non-fatal honeypot trap '{$attribute}' filled - suspicious bot activity", [
				'ip' => $userIp,
				'userAgent' => $userAgent,
				'attribute' => $attribute,
				'method' => __METHOD__
			]);
		}
	}
	
	/**
	* Adds virtual attribute to store errors related to honeypots
	*/
	public function attributes()
	{
		return array_merge(parent::attributes(), ['_honeypots']);
	}
}
```
and the view code
```
<?php
/**
* Honeypot
* @see app\components\traits\HoneypotTrait.php
*
* CSS class to hide honeypot inputs outside of viewport
*/
$css = <<<CSS
.hp-hidden,
.hp-hidden input,
.hp-hidden textarea
{
	position: absolute !important;
	left: -9999px !important;
	top: -9999px !important;
	height: 1px !important;
	width: 1px !important;
	overflow: hidden !important;
	pointer-events: none !important;
}
CSS;

$this->registerCss($css);
?>

<?php
/**
* Honeypot
* @see app\components\HoneypotManager.php
* @see app\components\traits\HoneypotTrait.php
*
* Render honeypot fields hidden
*/
$placeholders = $model::honeypotPlaceholders();

foreach ($hpFields as $field) :
	$placeholder = $placeholders[$field] ?? "Enter " . ucfirst($field);
	$label = $labels[$field] ?? ucfirst($field);
?>
<div class="col-md-12 mb-2">
<?= $form->field($model, $field, ['options' => ['class' => 'hp-hidden']])
	->textInput([
		//'tabindex' => '-1',
		//'autocomplete' => 'off',
		'placeholder' => $placeholder,
		'class' => 'form-control square',
])->label($label);
?>
</div><!-- .col-md-12 .mb-2 -->

<?php endforeach; ?>

<?php
// For debugging only, includes fatal honeypot names in HTML comments
if (YII_ENV_DEV && !empty($hpFatalFields)) :
?>
<pre style="color:#888;font-size:11px;">Fatal honeypots: <?= implode(', ', $hpFatalFields) ?></pre>

<?php endif; ?>
```

I would ike the render the css either in the honeypot class or honeypot trait and than call to be in the view somehow.

To centralize the honeypot CSS registration inside the HoneypotManager or HoneypotTrait and then call it from the view (or controller), you can refactor and expose a method that registers the CSS styles.

---

### Option 1: Put CSS registration method in `HoneypotManager`

1. Add a public method like `registerHoneypotCss(View $view)` inside the HoneypotManager class.

```php
use yii\web\View;

class HoneypotManager
{
    // existing code...

    /**
    * Registers the honeypot CSS needed to hide the honeypot fields (off-screen).
    * Use $view->registerCss() to inject the styles into the page.
    * 
    * @param View $view Yii view object where CSS will be registered.
    */
    public function registerHoneypotCss(View $view): void
    {
        $css = <<<CSS
.hp-hidden,
.hp-hidden input,
.hp-hidden textarea
{
    position: absolute !important;
    left: -9999px !important;
    top: -9999px !important;
    height: 1px !important;
    width: 1px !important;
    overflow: hidden !important;
    pointer-events: none !important;
}
CSS;
        $view->registerCss($css);
    }
}
```

2. Then in your **view code**, you call this method before rendering the fields:

```php
<?php
// Assuming $honeypotManager is passed to the view or accessible via Yii::$app->components

$honeypotManager->registerHoneypotCss($this);  // $this is View in Yii2 view files

?>

<!-- then continue with honeypot field render -->
```

---

### Option 2: Put CSS registration method in the Trait (`HoneypotTrait`)

Trait methods cannot receive the View automatically but you can pass the View object or call it through Yii::$app->view in the method:

```php
trait HoneypotTrait
{
    // Existing code...

    /**
    * Registers the honeypot CSS styles to hide traps.
    * @param \yii\web\View|null $view Optional view object, defaults to current application view.
    */
    public function registerHoneypotCss(?\yii\web\View $view = null): void
    {
        $view = $view ?: \Yii::$app->view;

        $css = <<<CSS
.hp-hidden,
.hp-hidden input,
.hp-hidden textarea
{
    position: absolute !important;
    left: -9999px !important;
    top: -9999px !important;
    height: 1px !important;
    width: 1px !important;
    overflow: hidden !important;
    pointer-events: none !important;
}
CSS;

        $view->registerCss($css);
    }
}
```

### Usage in the view

Assuming `$model` uses this trait:

```php
<?php
$model->registerHoneypotCss();
?>

<!-- Continue rendering the form and honeypot fields -->
```

---

### Summary

- Put a method either in `HoneypotManager` or `HoneypotTrait` that registers the CSS via Yii's View.
- In the view, just call `$honeypotManager->registerHoneypotCss($this)` or `$model->registerHoneypotCss()`.
- Keep all CSS related to honeypots centralized for easy edits and reuse.

---

If you want my help to merge this into your classes with full code snippets, just ask!

I would prefer to call to setup the css in the controller

Got it! You want to **register the honeypot CSS styles from the controller**, so when rendering your view the CSS is already included.

---

### How to implement

1. Add a public method in your **`HoneypotManager`** that registers the CSS into the view (like I showed):

```php
use yii\web\View;

class HoneypotManager
{
    // existing code ...

    /**
    * Registers the honeypot CSS styles in the provided view.
    *
    * @param View $view
    */
    public function registerHoneypotCss(View $view): void
    {
        $css = <<<CSS
.hp-hidden,
.hp-hidden input,
.hp-hidden textarea
{
    position: absolute !important;
    left: -9999px !important;
    top: -9999px !important;
    height: 1px !important;
    width: 1px !important;
    overflow: hidden !important;
    pointer-events: none !important;
}
CSS;

        $view->registerCss($css);
    }
}
```

2. In your **controller action**, when you create your HoneypotManager instance and prepare the honeypots, **register the CSS at that moment**:

```php
public function actionContact()
{
    $model = new \app\models\ContactForm();

    $honeypotPools = [
        \app\models\ContactForm::class => ['firstname', 'lastname', 'username', 'email', 'phone',
            'address', 'company', 'website', 'city', 'zip', 'comment'],
    ];

    $honeypotManager = new \app\components\HoneypotManager($honeypotPools, 2, 5);
    $hpData = $honeypotManager->prepareHoneypots($model);
    $hpFields = $hpData['displayed'];
    $hpFatalFields = $hpData['fatal'];

    // Register honeypot CSS in the view here:
    $honeypotManager->registerHoneypotCss($this->getView());

    $request = \Yii::$app->request;

    if ($honeypotManager->handleRequest($model, $hpFields)) {
        if ($model->process()) {
            \Yii::$app->session->setFlash('success', 'Thanks for contacting us.');
            return $this->refresh();
        }

        if ($model->isFatalHoneypotFilled()) {
            $model->status = 'spam';
            $model->save(false);
        }

        \Yii::$app->session->setFlash('error', 'Failed to process form.');
    }

    return $this->render('contact', [
        'model' => $model,
        'hpFields' => $hpFields,
        'hpFatalFields' => $hpFatalFields,
    ]);
}
```

---

### Explanation

- `$this->getView()` from within a controller action returns the view object for registering CSS or JS.
- When you call `$honeypotManager->registerHoneypotCss($this->getView())`, it **injects the CSS into the view** for your action.
- Then in your view, you just render the fields normally — no need to register CSS again.

---

### Optional: Cache / compression?

Since your original HoneypotManager uses caching/compression flags, you can extend the `registerHoneypotCss()` method to handle that if you want.

---

**That’s it!** You keep the CSS logic in honeypot class and register it neatly in your controller action.

Let me know if you want me to prepare a pull-ready patch with this integration!

public function getCartItems(): array
	{
		$this->checkInventory();
		
		$cartItems = Cart::find()
			->with(['content'/*, 'content.category'*/])// Eager load products to avoid N+1 queries
			->where(['created_by' => $this->_userId])
			->all();
		
		$results = [];
		
		foreach ($cartItems as $cartItem)
		{
			$product = $cartItem->content;
			
			if (!$product)
			{
				continue;
			}
			
			$result[] = [
				'id' => $cartItem->product_id,
				'quantity' => $cartItem->quantity,
				'total_price' => $cartItem->total_price,
				
				// Required data for cart handling
				'available_quantity' => (int) $product->quantity,
				//'total_price' => round($cartItem->quantity* $product->price, 2),
				'total_price' => MoneyHelper::totalAmount($cartItem->quantity, $product->price),
				
				/**
				* Content `product` are fetched as ActiveRecord and indexed by ID for quick access.
				* @see app\controllers\CartController.php
				* @see app\views\cart\index.php
				*
				*	// Fetch all active product records for items in $model->items
				* 	$productIds = array_column($model->items, 'productId');
				* 	$products = Content::find()
				* 		->id($productIds)
				* 		->ofType('product')
				* 		->indexBy('id')
				* 		->all();
				* 	
				* 	return $this->render('index', [
				* 		'model' => $model,
				* 		'products' => $products,
				* 	]);
				*
				* Note: Do not send anymore data, than what is 100% necessary.
				*
				'title' => $product->title,
				'template' => $product->template,
				'category_slug' => $product->category->slug ?? null,
				'slug' => $product->slug,
				'price' => (string)$product->price,
				'product_image' => $product->product_image_one,//*/
			];
		}
		
		return $result;
	}

 PHP Warning – yii\base\ErrorException
Undefined variable $result

The error:

```
Undefined variable $result
```

happens because you're using the variable `$result` for collecting data in the loop but never initialized it before the loop starts. Instead, you initialized `$results = [];` (with an **s**), but never used it.

---

### How to fix:

Either:

- Change `$results = [];` to `$result = [];`

or better:

- Consistently use `$results` throughout:

```php
public function getCartItems(): array
{
    $this->checkInventory();

    $cartItems = Cart::find()
        ->with(['content'])
        ->where(['created_by' => $this->_userId])
        ->all();

    $results = [];  // notice plural 'results'

    foreach ($cartItems as $cartItem) {
        $product = $cartItem->content;

        if (!$product) {
            continue;
        }

        $results[] = [        // <- Use $results here, not $result
            'id' => $cartItem->product_id,
            'quantity' => $cartItem->quantity,
            'total_price' => $cartItem->total_price,

            // Required data for cart handling
            'available_quantity' => (int) $product->quantity,
            'total_price' => MoneyHelper::totalAmount($cartItem->quantity, $product->price),

            'title' => $product->title,
            'template' => $product->template,
            'category_slug' => $product->category->slug ?? null,
            'slug' => $product->slug,
            'price' => (string)$product->price,
            'product_image' => $product->product_image_one,
        ];
    }

    return $results; // return plural variable
}
```

---

### Explanation

- You declared `$results = [];` to collect all cart items.
- Inside the loop you appended to `$result[] = ...` (without **s**), so PHP treats `$result` as a new local variable, which never got initialized.
- Then at the end, you return `$result` which is undefined (because `$result` was never defined outside the loop).

---

### TL;DR

Use the same variable name consistently, for example:

```php
$results = [];
...
$results[] = ...;
...
return $results;
```

---

If you want, I can help reformat or optimize the function further.

I have some quiz questions that include several scenarios. I was you to make the questions much more interesting and engaging

I'd be happy to help with that! Please share the quiz questions and scenarios you have, and I'll work on making them more interesting and engaging.

Team Member 	Questions 	Choices 
Ben 	Scenario: 
An alert was triggered on ABC Limited. The TM alert is Account Triggered. After the investigative review, a No Reportable Activity conclusion was reached. The activity was consistent with the customer profile, and only a First Level Review was required. 
Question: 
Which of the following Persons of Interest (POIs) do you need to profile? 
 	A. ABC Limited 
B. ABC Limited and John Smith (Account Holder) 
C. John Smith, Jane Smith and Adam Smith (Beneficial Owners identified in Commsee) 
D. ABC Limited, John Smith, Jane Smith and Adam Smith 
 
Ben 	Scenario: 
You are conducting an account review for LMN Enterprises due to an alert triggered relating to transactions that occurred 5 months ago.  
Question: 
What should be the appropriate look-back period for this account review? 
 	A. 3 months prior from today 
B. 5 months prior from today 
C. Only the date of the alert-triggered transactions (5 months ago) only 
D. 3 months prior from today plus a focused review on the single transaction 5 months ago. 
 

Team Member 	Questions 	Choices 	Category of QA error covered 
Harrison 	Scenario:  
You are closing a TM alert involving a business customer under the name of 'Global Transfers Pty Ltd' as not suspicious. The TM alert is Account Triggered. A duplicate profile was found on the customer.  
 
Question: 
Which of the following is most correct? 	A.	Duplicate profile does not need to be added into investigation as there was no activity found on the duplicate profile 
B.	Beneficial owners are not required to be added into scope, only the entity is required to be profiled 
C.	Beneficial owners would need to be added into scope.  
D.	The director, an account owner of the triggered activity, is not required to be added into investigation   	Insufficient information collected to understand the customer profile 
 
CP missed review of POI or BO or Signatory 
Harrison 	Scenario: 
 
You have discovered that through open source search via mobile number, there is adverse media on your customer regarding money laundering charges this year. The customer has an address with links to a residential property in Brisbane. A preferred name was also found, returning adverse media findings.  
 
Question: 
Which of the following is most correct? 	A.	Adverse media findings through mobile number search need to be referenced 
B.	Results of address search need to be documented in Sirius as this is relevant to the investigation 
A.	Adverse media findings through mobile number search and preferred name need to be documented 
B.	All of the above 	Did not conduct and/or evidence all relevant open source searches to build the profile of the customer 
 
CP missed conducting OSS 
CP missed/incorrectly assessed OSS search 
 


Team Member 	Questions 	Choices 	Category of QA error covered 
Khoa 	You are conducting a review for a TM alert and you discover that your Main triggering event customer (TEC) has a preferred name of Diddy Combs (Full Name - Sean John Combs).  
 
Question: 
What name(s) are you screening for ABN and AUSTRAC? 	A.	Customer's preferred name only 
B.	Customer's preferred and full legal name 
C.	Customer's full legal name only 
D.	Customer's legal first and last name only 	CP missed ABN match 
CP missed AUSTRAC remitter search 
CP incomplete customer profile captured 
Insufficient information collected to understand the customer profile. 
Khoa 	Open-source search has identified adverse media for CEM concerns relating to TEC John Smith. Alert was triggered for rapid DEFT movement within his accounts. A review of Combs's accounts identified structured cash deposits followed by outward DEFTs to CBA minors. 
 
Question:
During our investigation, where can we source and map the relevant PI parties? 
	A.	Authorities tab, related customers tab & documents 
B.	Transactional nexus 
C.	Adverse media  
D.	All of the above  	 

 
Team Member 	Questions 	Choices 	Category of QA error covered  
Steve 	You are conducting an ABN search. What parameter would you use?  	A.	Customer's CIF. 
B.	Customer's ABN only. 
C.	Customer's name only. 
D.	Customer’s ABN where possible. If no ABN is available, then customer’s name. 	 
Steve 	Why is location an important factor when profiling an entity ?  	A.	To understand the culture background of the people who live in that location.  
B.	So we can visit them on our free time.  
C.	To understand the property value.  
D.	Location is not important when it comes to TM.  
	 


Team Member 	Questions 	Choices 	Category of QA error covered 
 
Danh  	You receive the below information from an LEA notice: 
 
Period of interest: 2009 to 2025 
 
Law Enforcement believe the customer has made a name change in 2010.   
 
Former name: Joe Rogan 
Current name: Bro Jogan 
 
What is the next course of action ? 	A) As the name change occurred more than 10 year ago, close the case as a FP.  
 
B) Conduct a search on Commsee to see whether a profile exists under the former name. Where available, complete a full profile review and add to Part C. Do same for current name. 
 
C) Conduct OSS on the former name only. 
 
D) Conduct a search on Commsee to see whether a profile exists under the former name but not complete a profile review. 	CP did not consider information in a duplicate profile where applicable  
Danh 	You have completed a full review on your triggering alert customer which happens to be a company and have determined you will submit an SMR.  
Which of the following parties must you include and conduct a review on ? 	A) All Beneficial Owners of the company listed on Commsee. 
 
B) Any Money Trail AP you have identified as complicit in your transaction review. 
 
C) Any party which has received funds from the company. 
 
D) A & B only 	CP missed review of POI or BO or Signatory 
 


Team Member 	Questions 	Choices 	Category of QA error covered 
Alan 	You have an alert triggered for LEA notice received requesting for information on POI’s accounts and any accounts whereby he is a signatory of from 01/01/2024. Which accounts are in scope of your review? 
 	A.	Active products held by POI 
B.	Closed products held by POI from 01/01/2024 
C.	accounts where he held authority on from 01/01/2024 
D.	All the above  	 
Alan 	POI was triggered based on LEA notice from police for fraud investigations. Through OSS search you have only identified a court list match for your POI, what is the appropriate action you should take? 
 	A.	Record and attach the article, articulate there is information found on court list match 
B.	No action is required given it is not an adverse article 
C.	Add a pulse note that there is court l/ist found, however no adverse concerns 
D.	Attach the article and record it as material, it is reasonable to believe customer was involved in fraud offences given the court list match combined with LEA investigation 	Immateriality of adverse media not captured 
 
 

Thanks for sharing these! To make your quiz questions more interesting and engaging, we can add storytelling elements, real-world contexts, or dilemmas that require deeper thought. Here’s a revamped version of your questions with a stronger narrative flow, more engaging language, and thought-provoking options to enhance learner involvement.

---

### Ben’s Questions

**Scenario 1:**
*You receive an alert for ABC Limited. The trigger is "Account Triggered," but after a thorough investigation, you conclude there is no reportable activity. The transactions align perfectly with ABC Limited’s customer profile, and only a First Level Review was necessary.*

**Question:**  
*Who should you include in your profiling to maintain compliance and thoroughness?*  

A. ABC Limited only – the entity itself is enough.  
B. ABC Limited and John Smith, the Account Holder.  
C. John Smith, Jane Smith, and Adam Smith, the Beneficial Owners identified in Commsee.  
D. ABC Limited, John Smith, Jane Smith, and Adam Smith – leaving no stone unturned.

---

**Scenario 2:**  
*You’re preparing to conduct an account review for LMN Enterprises after an alert related to transactions that took place 5 months ago was triggered.*

**Question:**  
*What is the most appropriate look-back period to ensure your review captures all relevant activity?*  

A. Only the last 3 months, since recent activity is key.  
B. 5 months prior from today, to fully cover the alert period.  
C. Just the date of the alerted transactions (5 months ago).  
D. The last 3 months plus a targeted review of the transaction from 5 months ago to catch any hidden patterns.

---

### Harrison’s Questions

**Scenario 1:**  
*You’re closing an account-triggered alert for 'Global Transfers Pty Ltd.' You discover a duplicate customer profile in the system during investigation.*

**Question:**  
*Which of the following reflects best practice regarding handling the duplicate profile in the investigation?*  

A. Since the duplicate profile has no activity, it can be ignored.  
B. Only the entity needs profiling; beneficial owners are not required.  
C. Beneficial owners must be included in the scope — profile everyone involved thoroughly.  
D. Directors who own the account but are not linked to triggered activities are irrelevant to the investigation.

*(Hint: This touches on insufficient information captured about the customer profile.)*

---

**Scenario 2:**  
*While conducting open-source searches (OSS), you uncover adverse media linked to your customer via their mobile number, address, and even their preferred alias.*

**Question:**  
*What should be documented to ensure you meet all compliance and investigative standards?*  

A. Reference only adverse media found through the mobile number search.  
B. Document address-related adverse media findings in Sirius.  
C. Record adverse media uncovered via both mobile number and preferred name searches.  
D. Document all findings above comprehensively — nothing should be missed.

---

### Khoa’s Questions

**Scenario 1:**  
*You notice that the main triggering event customer (TEC) uses the preferred name "Diddy Combs," but their legal name is Sean John Combs.*

**Question:**  
*Which names should you screen against ABN and AUSTRAC databases to ensure thoroughness?*  

A. Preferred name only.  
B. Both preferred name and full legal name.  
C. Full legal name only.  
D. First and last legal name only.

---

**Scenario 2:**  
*Upon open-source research, adverse media is found against TEC John Smith related to cash deposits followed by structured transfers to minor accounts.*

**Question:**  
*Where is the best place to source and map all relevant Persons of Interest for a comprehensive investigation?*  

A. Authorities tab, Related Customers tab, and Documents section.  
B. Transactional nexus records.  
C. Adverse media reports.  
D. All of the above — combining data sources reveals the full picture.

---

### Steve’s Questions

**Scenario 1:**  
*When performing an ABN search on a customer to verify their identity...*

**Question:**  
*Which search parameter should you prioritize to obtain the most accurate information?*  

A. Customer’s CIF (Customer Information File).  
B. Customer’s ABN only.  
C. Customer’s name only.  
D. Customer’s ABN primarily, but if unavailable, use their name as a fallback.

---

**Scenario 2:**  
*Why is a customer’s geographical location a critical factor when profiling an entity?*  

A. It helps understand cultural backgrounds influencing financial behaviors.  
B. So you know where to visit on your free time!  
C. To assess property values and asset backing.  
D. Location is irrelevant in transaction monitoring.

---

### Danh’s Questions

**Scenario 1:**  
*You receive an LEA notice ranging from 2009 to 2025. The customer reportedly changed their name in 2010 from Joe Rogan to Bro Jogan.*

**Question:**  
*What is your next course of action to ensure no stone is left unturned in your investigation?*  

A. Close the case immediately as a false positive since the name change was over 10 years ago.  
B. Perform a Commsee search for profiles under both former and current names, completing full reviews where found.  
C. Conduct Open Source Searches only on the former name.  
D. Search for former name only; do not conduct full profile reviews.

---

**Scenario 2:**  
*After a full review on a company triggering an alert, you decide to file an SMR.*

**Question:**  
*Which parties must you include and review to ensure your report is complete?*  

A. All Beneficial Owners listed on Commsee.  
B. Any Money Trail Associated Parties identified in the transaction review.  
C. Any customer who received funds from the company.  
D. Both A and B — beneficiaries and complicit parties.

---

### Alan’s Questions

**Scenario 1:**  
*An LEA notice requests info on a POI's accounts and any accounts where they are signatory from 01/01/2024.*

**Question:**  
*What accounts should be in scope for your review?*  

A. Only active products held by the POI.  
B. Closed products held by the POI since 01/01/2024.  
C. Accounts where POI had authority from 01/01/2024.  
D. All of the above — a comprehensive scan.

---

**Scenario 2:**  
*Your OSS search for the POI reveals a court list match related to fraud.*

**Question:**  
*What is the appropriate next step in handling this information?*  

A. Attach and record the article, highlighting its relevance to the investigation.  
B. No action, as the article isn’t adverse media.  
C. Add a pulse note noting the court list match without escalating.  
D. Treat as material evidence combined with LEA notice and record it accordingly.

---

If you'd like, I can also help with formatting, adding visuals, or integrating scenario-based branching questions to deepen engagement! Would you like me to prepare that next?