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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    IndexError
Message:      list index out of range
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1859, in _prepare_split_single
                  original_shard_lengths[original_shard_id] += len(table)
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
              IndexError: list index out of range
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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trace: hashing model and predictor before loading weights
trace placement: routed_experts=CPU n_gpu_layers=999 hot_cache=off
ggml_cuda_init: found 1 CUDA devices (Total VRAM: 32109 MiB):
Device 0: NVIDIA GeForce RTX 5090, compute capability 12.0, VMM: yes, VRAM: 32109 MiB
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 5090) (0000:02:00.0) - 31602 MiB free
llama_model_loader: loaded meta data with 54 key-value pairs and 404 tensors from /home/zhanghanming/models/DeepSeek-V2-Lite-Chat.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = deepseek2
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 3: general.sampling.temp f32 = 0.300000
llama_model_loader: - kv 4: general.name str = DeepSeek V2 Lite Chat
llama_model_loader: - kv 5: general.finetune str = Chat
llama_model_loader: - kv 6: general.basename str = DeepSeek-V2-Lite
llama_model_loader: - kv 7: general.size_label str = 64x1.5B
llama_model_loader: - kv 8: general.license str = other
llama_model_loader: - kv 9: general.license.name str = deepseek
llama_model_loader: - kv 10: general.license.link str = https://github.com/deepseek-ai/DeepSe...
llama_model_loader: - kv 11: deepseek2.block_count u32 = 27
llama_model_loader: - kv 12: deepseek2.context_length u32 = 163840
llama_model_loader: - kv 13: deepseek2.embedding_length u32 = 2048
llama_model_loader: - kv 14: deepseek2.feed_forward_length u32 = 10944
llama_model_loader: - kv 15: deepseek2.attention.head_count u32 = 16
llama_model_loader: - kv 16: deepseek2.attention.head_count_kv u32 = 1
llama_model_loader: - kv 17: deepseek2.rope.scaling.type str = yarn
llama_model_loader: - kv 18: deepseek2.rope.scaling.factor f32 = 40.000000
llama_model_loader: - kv 19: deepseek2.rope.scaling.original_context_length u32 = 4096
llama_model_loader: - kv 20: deepseek2.rope.scaling.yarn_beta_fast f32 = 32.000000
llama_model_loader: - kv 21: deepseek2.rope.scaling.yarn_beta_slow f32 = 1.000000
llama_model_loader: - kv 22: deepseek2.rope.freq_base f32 = 10000.000000
llama_model_loader: - kv 23: deepseek2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 24: deepseek2.expert_used_count u32 = 6
llama_model_loader: - kv 25: deepseek2.expert_group_count u32 = 1
llama_model_loader: - kv 26: deepseek2.expert_group_used_count u32 = 1
llama_model_loader: - kv 27: deepseek2.expert_gating_func u32 = 1
llama_model_loader: - kv 28: deepseek2.attention.key_length u32 = 576
llama_model_loader: - kv 29: deepseek2.attention.value_length u32 = 512
llama_model_loader: - kv 30: general.file_type u32 = 1
llama_model_loader: - kv 31: deepseek2.leading_dense_block_count u32 = 1
llama_model_loader: - kv 32: deepseek2.vocab_size u32 = 102400
llama_model_loader: - kv 33: deepseek2.attention.kv_lora_rank u32 = 512
llama_model_loader: - kv 34: deepseek2.attention.key_length_mla u32 = 192
llama_model_loader: - kv 35: deepseek2.attention.value_length_mla u32 = 128
llama_model_loader: - kv 36: deepseek2.expert_feed_forward_length u32 = 1408
llama_model_loader: - kv 37: deepseek2.expert_count u32 = 64
llama_model_loader: - kv 38: deepseek2.expert_shared_count u32 = 2
llama_model_loader: - kv 39: deepseek2.expert_weights_scale f32 = 1.000000
llama_model_loader: - kv 40: deepseek2.rope.dimension_count u32 = 64
llama_model_loader: - kv 41: deepseek2.rope.scaling.yarn_log_multiplier f32 = 0.070700
llama_model_loader: - kv 42: general.quantization_version u32 = 2
llama_model_loader: - kv 43: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 44: tokenizer.ggml.pre str = deepseek-llm
llama_model_loader: - kv 45: tokenizer.ggml.tokens arr[str,102400] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 46: tokenizer.ggml.token_type arr[i32,102400] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 47: tokenizer.ggml.merges arr[str,99757] = ["Ġ Ġ", "Ġ t", "Ġ a", "i n", "h e...
llama_model_loader: - kv 48: tokenizer.ggml.bos_token_id u32 = 100000
llama_model_loader: - kv 49: tokenizer.ggml.eos_token_id u32 = 100001
llama_model_loader: - kv 50: tokenizer.ggml.padding_token_id u32 = 100001
llama_model_loader: - kv 51: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 52: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 53: tokenizer.chat_template str = {% if not add_generation_prompt is de...
llama_model_loader: - type f32: 108 tensors
llama_model_loader: - type f16: 296 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = F16
print_info: file size = 29.26 GiB (16.00 BPW)
load: 0 unused tokens
load: printing all EOG tokens:
load: - 100001 ('<|end▁of▁sentence|>')
load: special tokens cache size = 2
load: token to piece cache size = 0.6408 MB
print_info: arch = deepseek2
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 163840
print_info: n_embd = 2048
print_info: n_embd_inp = 2048
print_info: n_layer = 27
print_info: n_head = 16
print_info: n_head_kv = 1
print_info: n_rot = 64
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 576
print_info: n_embd_head_v = 512
print_info: n_gqa = 16
print_info: n_embd_k_gqa = 576
print_info: n_embd_v_gqa = 512
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 10944
print_info: n_expert = 64
print_info: n_expert_used = 6
print_info: n_expert_groups = 1
print_info: n_group_used = 1
print_info: causal attn = 1
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