huggingface Errors

101 entries

Error Code Domain Category Fix Rate Confidence Tier
RuntimeError: Accelerate device placement error — huggingface device_error 80% 84% ai_generated
UserWarning: We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting — huggingface input_error 90% 92% ai_generated
OutOfMemoryError during attention computation — huggingface memory_error 78% 82% ai_generated
HTTPError: 401 Unauthorized - gated model — huggingface auth_error 90% 92% ai_generated
ImportError: bitsandbytes not found or CUDA not available — huggingface import_error 85% 88% ai_generated
TemplateError: chat template rendering failed — huggingface config_error 82% 86% ai_generated
ValueError: Unrecognized configuration class or invalid config parameter for model type — huggingface configuration_error 85% 88% ai_generated
OutOfMemoryError: CUDA out of memory (Transformers) — huggingface memory_error 80% 85% ai_generated
ValueError: Loading this model requires trust_remote_code=True — huggingface config_error 85% 88% ai_generated
FileNotFoundError: dataset script not found — huggingface resource_error 82% 85% ai_generated
ConnectionError: streaming dataset connection lost — huggingface io_error 78% 82% ai_generated
RuntimeError: expected Float but got Half during training — huggingface type_error 82% 86% ai_generated
ValueError: metric computation failed — huggingface runtime_error 80% 84% ai_generated
ImportError: Flash Attention 2 not compatible with this model — huggingface config_error 80% 84% ai_generated
HTTPError: 401 Client Error: Unauthorized for url: huggingface.co — huggingface auth_error 92% 90% ai_generated
ValueError: generation config has conflicting parameters — huggingface config_error 82% 86% ai_generated
torch.cuda.OutOfMemoryError: CUDA out of memory during model.generate() — huggingface memory_error 80% 88% ai_generated
RuntimeError: gradient checkpointing incompatible — huggingface training_error 78% 82% ai_generated
HTTPError: 503 Service Unavailable - model loading — huggingface timeout_error 75% 80% ai_generated
RuntimeError: cannot merge LoRA weights — huggingface runtime_error 78% 82% ai_generated
OSError: model-org/model-name is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models' — huggingface loading_error 90% 92% ai_generated
OnnxExporterError: model export to ONNX failed — huggingface export_error 78% 82% ai_generated
ValueError: PEFT version incompatible with adapter EFT huggingface version_error 82% 86% ai_generated
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! — huggingface device_error 88% 90% ai_generated
ValueError: RoPE scaling configuration is invalid — huggingface config_error 80% 84% ai_generated
OSError: Error no file named model.safetensors found in directory or unable to load safetensors file — huggingface loading_error 85% 88% ai_generated
OSError: error loading sharded checkpoint — huggingface io_error 78% 82% ai_generated
OSError: Can't load tokenizer for 'model-name'. Make sure the model identifier is correct. — huggingface loading_error 88% 90% ai_generated
Token indices sequence length is longer than the specified maximum sequence length for this model (2048 > 512). Running this sequence through the model will result in indexing errors — huggingface input_error 90% 90% ai_generated
ValueError: tokenizer and model vocab size mismatch — huggingface config_error 82% 85% ai_generated
ValueError: special tokens not properly configured — huggingface config_error 82% 86% ai_generated
RuntimeError: failed to save/load training checkpoint — huggingface io_error 78% 82% ai_generated
ImportError: model requires transformers>=4.38 — huggingface version_error 85% 88% ai_generated
wandb.errors.Error: wandb integration failed — huggingface integration_error 80% 84% ai_generated
ValueError: Text input must of type str (single example), List[str] (batch) — huggingface type_error 92% 92% ai_generated
ValueError: zero-shot classification labels invalid — huggingface input_error 85% 88% ai_generated
ValueError: safetensors metadata mismatched: expected 1024 tensors, got 1023 — huggingface data_error 85% 85% ai_generated
ValueError: The task `summarization_v2` is not supported by the pipeline. Supported tasks are: ['text-classification', 'token-classification', 'question-answering', 'summarization', 'translation', 'text-generation', 'zero-shot-classification', 'fill-mask', 'ner', 'sentiment-analysis'] — huggingface config_error 95% 88% ai_generated
ValueError: batch_size must be None for IterableDataset, but got 32 — huggingface type_error 90% 82% ai_generated
HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/api/models/org/nonexistent-model 404 huggingface network_error 95% 90% ai_generated
ValueError: repetition_penalty and no_repeat_ngram_size cannot be set simultaneously as they may conflict — huggingface config_error 90% 83% ai_generated
UserWarning: You are using a decoder-only model with padding_side='right'. This may produce incorrect results. Consider setting padding_side='left'. — huggingface config_error 95% 87% ai_generated
RuntimeError: PEFT adapter weight shape mismatch: expected [4096, 4096] but got [4096, 2048] — huggingface runtime_error 92% 88% ai_generated
RuntimeError: Dataset shuffling requires a deterministic seed for iterable datasets, but seed is None — huggingface data_error 88% 82% ai_generated
OSError: Unable to load model from cache: file hash mismatch for 'pytorch_model.bin'. Expected sha256: abc123... but got xyz789... — huggingface system_error 90% 85% ai_generated
TypeError: The `eval_dataset` must be a `Dataset` or `IterableDataset` object, but got <class 'list'> — huggingface type_error 93% 87% ai_generated
ValueError: Cannot use device_map='auto' with pipeline when device is explicitly set to 0. Please set device_map=None or remove device argument. — huggingface config_error 92% 90% ai_generated
RuntimeError: device_map='auto' is not supported when using Trainer with a model that has been loaded with device_map='auto'. Please set device_map=None or load the model on a single device. — huggingface config_error 90% 85% ai_generated
ValueError: You passed `quantization_config` with `bnb_4bit_compute_dtype=torch.float16` but the model weights are loaded in `torch.bfloat16`. This is incompatible. — huggingface config_error 90% 87% ai_generated
ValueError: Adding special tokens to a tokenizer that already has them; use `add_special_tokens=True` only if you intend to add new tokens. Got extra_ids=0 but tokenizer already has 2 special tokens. — huggingface config_error 90% 84% ai_generated
ValueError: The pipeline does not support chunked inference for this task. Set `chunked_inference=False` or use a compatible task like 'text-generation'. — huggingface runtime_error 90% 82% ai_generated
ValueError: Target modules ['q_proj', 'v_proj'] not found in the base model. Available modules are: ['query', 'value', 'key', 'output'] — huggingface config_error 85% 88% ai_generated
ValueError: The features of the dataset do not match the expected schema. Missing columns: ['text', 'label']. Extra columns: ['input', 'target'] — huggingface data_error 90% 85% ai_generated
UserWarning: You are using a decoder-only model with padding_side='right'. This may produce incorrect results. Consider setting `tokenizer.padding_side = 'left'`. — huggingface config_error 95% 90% ai_generated
TypeError: Streaming dataset does not have a known length. Use `len(dataset)` only on non-streaming datasets. — huggingface type_error 90% 87% ai_generated
ValueError: repetition_penalty and no_repeat_ngram_size cannot be set simultaneously as they may conflict. — huggingface config_error 90% 88% ai_generated
RuntimeError: You cannot set both `device_map` and `device` when using the `accelerate` launcher with multiple GPUs. — huggingface config_error 90% 86% ai_generated
Token indices sequence length is longer than the specified maximum sequence length for this model (2048 > 1024). Running out-of-order — huggingface runtime_error 85% 85% ai_generated
OSError: model-org/model-name is not a local folder and is not a valid model identifier listed on 'https://huggingface.co' — huggingface network_error 90% 88% ai_generated
ValueError: The task `summarization_v2` is not supported by the pipeline. Supported tasks are: ['text-classification', 'token-classification', 'question-answering', 'summarization', 'translation', 'text-generation', 'zero-shot-classification', 'fill-mask'] — huggingface config_error 95% 82% ai_generated
ValueError: The features of the dataset do not match the expected schema. Missing columns: ['text', 'label']. Extra columns: ['id', 'metadata'] — huggingface data_error 88% 86% ai_generated
ValueError: You passed `quantization_config` with `bnb_4bit_compute_dtype=torch.float16` but the model weights are loaded in torch.float32. This may cause unexpected behavior. — huggingface config_error 85% 83% ai_generated
RuntimeError: failed to save/load training checkpoint. Could not write file to /path/to/checkpoint — huggingface runtime_error 80% 81% ai_generated
RuntimeError: Placeholder storage has not been allocated on MPS device — huggingface runtime_error 80% 85% ai_generated
OSError: [Errno 84] Too many links while accessing dataset cache 84 huggingface system_error 85% 88% ai_generated
ValueError: The adapter configuration does not match the base model. Expected adapter type 'lora' but got 'ia3' — huggingface config_error 88% 85% ai_generated
RuntimeError: gradient_checkpointing requires use_cache=False — huggingface config_error 93% 90% ai_generated
ValueError: Loading this model requires trust_remote_code=True. See https://huggingface.co/docs/hub/security — huggingface config_error 93% 90% ai_generated
OSError: Unable to mmap dataset file: [Errno 12] Cannot allocate memory ENOMEM huggingface system_error 75% 85% ai_generated
ValueError: The combination of parameters `max_new_tokens` and `max_length` is ambiguous. Please set only one of them. — huggingface config_error 95% 90% ai_generated
RuntimeError: You are trying to train a 4-bit model but you have set `inference_mode=True`. Set `inference_mode=False` for training. — huggingface config_error 98% 88% ai_generated
TypeError: Text input must be of type `str` (single example), `List[str]` (batch) or `List[List[str]]` (batch of sequences). Received type: <class 'int'> — huggingface type_error 92% 87% ai_generated
ValueError: The number of outputs returned by the pipeline does not match the number of inputs. Expected 8 outputs, got 4. — huggingface runtime_error 80% 85% ai_generated
ValueError: An adapter with the name 'default' is already loaded. Use a unique adapter name or disable this adapter. — huggingface config_error 90% 88% ai_generated
UserWarning: Using `pad_token_id` but `padding_side` is not set. The tokenizer will use 'right' padding by default, which may not be optimal for decoder-only models. — huggingface config_error 95% 82% ai_generated
ValueError: `early_stopping` is set to True but `num_beams` is 1. Early stopping has no effect without beam search. — huggingface config_error 95% 87% ai_generated
ValueError: You passed `quantization_config` with `bnb_4bit_compute_dtype=torch.float16` but the model weights are loaded in `torch.float32`. This may cause numerical instability. — huggingface config_error 88% 86% ai_generated
ValueError: You passed `quantization_config` with `bnb_4bit_compute_dtype=torch.float16` but the model weights are loaded in torch.float32. This may cause numerical instability. — huggingface config_error 88% 86% ai_generated
IndexError: list index out of range while processing shard 4 of dataset 'my_dataset' — huggingface data_error 88% 85% ai_generated
RuntimeError: PEFT adapter 'my_adapter' not found in checkpoint. Available adapters: ['base', 'lora_v1'] — huggingface config_error 92% 88% ai_generated
JSONDecodeError: Expecting value: line 1 column 1 (char 0) while loading tokenizer config from 'tokenizer.json' — huggingface data_error 90% 87% ai_generated
RuntimeError: The number of batches in the dataloader (127) is not divisible by gradient_accumulation_steps (8). This may cause uneven gradient updates. — huggingface config_error 85% 84% ai_generated
ValueError: Truncation side 'left' is not compatible with padding side 'right' for this model. Set truncation_side='right' or padding_side='left'. — huggingface config_error 93% 89% ai_generated
UserWarning: You are using a decoder-only model with padding_side='right'. This may produce incorrect results. Consider setting padding_side='left' before tokenizing. — huggingface config_error 92% 88% ai_generated
RuntimeError: The base model dtype is torch.float32 but the LoRA adapter was trained with torch.bfloat16. This may cause numerical instability. — huggingface type_error 85% 87% ai_generated
KeyError: "Split 'validation' not found in dataset. Available splits: ['train', 'test']" — huggingface data_error 95% 90% ai_generated
ValueError: The sum of `max_new_tokens` (1000) and input length (512) exceeds the model's maximum position embeddings (2048). Reduce `max_new_tokens` or truncate input. — huggingface config_error 93% 89% ai_generated
TypeError: Expected feature type 'Value(dtype='int32')' but got 'Value(dtype='int64')' for column 'labels'. Cast the column to the correct dtype. — huggingface type_error 88% 86% ai_generated
ValueError: The task `text-generation` is not supported by the pipeline for this model. Supported tasks are: ['feature-extraction', 'fill-mask', 'sentence-similarity', 'text-classification']. — huggingface config_error 95% 90% ai_generated
OSError: Error while deserializing header: HeaderTooLarge — huggingface system_error 80% 85% ai_generated
UserWarning: The tokenizer's unknown token (unk_token) is set to '<unk>', but the model's embedding layer has 0 embeddings for it. This may cause issues during generation. — huggingface config_error 75% 82% ai_generated
HTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/models/gpt2 429 huggingface network_error 90% 88% ai_generated
ValueError: Early stopping requires `load_best_model_at_end=True` in the training arguments, but it is set to False. — huggingface config_error 95% 90% ai_generated
RuntimeError: torch.compile is not supported for PEFT models with dynamic adapters — huggingface runtime_error 80% 85% ai_generated
ValueError: pad_token_id (0) must be within the vocabulary size (32000) but is greater than or equal to the vocab size — huggingface config_error 85% 88% ai_generated
RuntimeError: Trainer callback hook 'on_log' failed with AttributeError: 'NoneType' object has no attribute 'step' — huggingface runtime_error 80% 82% ai_generated
RuntimeError: expected all tensors to be on the same device, but model parameters have been offloaded to CPU — huggingface runtime_error 80% 85% ai_generated
ValueError: The batch size of the pipeline (8) does not match the batch size of the dataset (32). Please set batch_size=None or ensure they match. — huggingface config_error 90% 88% ai_generated
ValueError: Target modules ['q_proj', 'v_proj'] not found in the base model. Available modules: ['query', 'value', 'key', 'output'] — huggingface config_error 85% 90% ai_generated
UserWarning: The tokenizer is a 'PreTrainedTokenizerFast' but the model was loaded with a slow tokenizer. This may cause encoding inconsistencies. — huggingface config_error 90% 82% ai_generated
ValueError: You passed `quantization_config` with `bnb_4bit_compute_dtype=torch.float16` but the model weights are loaded in torch.float32. Set `bnb_4bit_compute_dtype` to match the model weights or convert the model. — huggingface config_error 88% 86% ai_generated