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Browse files- README.md +20 -0
- adapter_config.json +21 -0
- adapter_model.bin +3 -0
- all_results.json +7 -0
- checkpoint-1000/README.md +20 -0
- checkpoint-1000/adapter_config.json +21 -0
- checkpoint-1000/adapter_model.bin +3 -0
- checkpoint-1000/optimizer.pt +3 -0
- checkpoint-1000/rng_state_0.pth +3 -0
- checkpoint-1000/rng_state_1.pth +3 -0
- checkpoint-1000/scheduler.pt +3 -0
- checkpoint-1000/special_tokens_map.json +9 -0
- checkpoint-1000/tokenization_internlm.py +242 -0
- checkpoint-1000/tokenizer.model +3 -0
- checkpoint-1000/tokenizer_config.json +16 -0
- checkpoint-1000/trainer_state.json +616 -0
- checkpoint-1000/training_args.bin +3 -0
- checkpoint-2000/README.md +20 -0
- checkpoint-2000/adapter_config.json +21 -0
- checkpoint-2000/adapter_model.bin +3 -0
- checkpoint-2000/optimizer.pt +3 -0
- checkpoint-2000/rng_state_0.pth +3 -0
- checkpoint-2000/rng_state_1.pth +3 -0
- checkpoint-2000/scheduler.pt +3 -0
- checkpoint-2000/special_tokens_map.json +9 -0
- checkpoint-2000/tokenization_internlm.py +242 -0
- checkpoint-2000/tokenizer.model +3 -0
- checkpoint-2000/tokenizer_config.json +16 -0
- checkpoint-2000/trainer_state.json +1216 -0
- checkpoint-2000/training_args.bin +3 -0
- special_tokens_map.json +9 -0
- tokenization_internlm.py +242 -0
- tokenizer.model +3 -0
- tokenizer_config.json +16 -0
- train_results.json +7 -0
- trainer_log.jsonl +216 -0
- trainer_state.json +1315 -0
- training_args.bin +3 -0
- training_loss.png +0 -0
README.md
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---
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library_name: peft
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---
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## Training procedure
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The following `bitsandbytes` quantization config was used during training:
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- load_in_8bit: False
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- load_in_4bit: True
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- llm_int8_threshold: 6.0
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- llm_int8_skip_modules: None
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- llm_int8_enable_fp32_cpu_offload: False
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- llm_int8_has_fp16_weight: False
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- bnb_4bit_quant_type: nf4
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- bnb_4bit_use_double_quant: True
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- bnb_4bit_compute_dtype: float16
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### Framework versions
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- PEFT 0.4.0
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adapter_config.json
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{
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"auto_mapping": null,
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"base_model_name_or_path": "../internlm-chat-20b",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layers_pattern": null,
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"layers_to_transform": null,
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"lora_alpha": 32.0,
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"lora_dropout": 0.1,
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 8,
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"revision": null,
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"target_modules": [
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"q_proj",
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"v_proj"
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],
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"task_type": "CAUSAL_LM"
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}
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adapter_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:8692759d0e4907942aecd47602d501e3c0d99b8481710db9efa4c347ae2bbecb
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size 39407885
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all_results.json
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{
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"epoch": 2.0,
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"train_loss": 1.4111853902105498,
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"train_runtime": 51425.1791,
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"train_samples_per_second": 2.684,
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"train_steps_per_second": 0.042
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}
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checkpoint-1000/README.md
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---
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library_name: peft
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---
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## Training procedure
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The following `bitsandbytes` quantization config was used during training:
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- load_in_8bit: False
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+
- load_in_4bit: True
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+
- llm_int8_threshold: 6.0
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- llm_int8_skip_modules: None
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- llm_int8_enable_fp32_cpu_offload: False
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+
- llm_int8_has_fp16_weight: False
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- bnb_4bit_quant_type: nf4
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- bnb_4bit_use_double_quant: True
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- bnb_4bit_compute_dtype: float16
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### Framework versions
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- PEFT 0.4.0
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checkpoint-1000/adapter_config.json
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{
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"auto_mapping": null,
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"base_model_name_or_path": "../internlm-chat-20b",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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+
"layers_pattern": null,
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"layers_to_transform": null,
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"lora_alpha": 32.0,
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+
"lora_dropout": 0.1,
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 8,
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"revision": null,
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"target_modules": [
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"q_proj",
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"v_proj"
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],
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"task_type": "CAUSAL_LM"
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}
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checkpoint-1000/adapter_model.bin
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:4998260bd526a9c3bee94abe2e54be3fec53559ce12bc659c21899480083d3e6
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size 39407885
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checkpoint-1000/optimizer.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:5fa4225c75d79ec4376d38b077a7cdff57bb875e08cc33c5d567919695684878
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size 78784709
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checkpoint-1000/rng_state_0.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:eb09c0ab810491cec8dcbb357118e13006f5424ce1d217c048964a2052a80e9f
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size 18679
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checkpoint-1000/rng_state_1.pth
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:addb671ff88a73231dd9b55b4517a019907c186cc25e81845c679685a9459723
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size 18679
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checkpoint-1000/scheduler.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:b3cf3e0db7ad9e9ba5dd577b3481b4ddbb58c5600860f207a1a04a3a4ee97a42
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size 627
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checkpoint-1000/special_tokens_map.json
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{
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"additional_special_tokens": [
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"<eoa>"
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],
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"bos_token": "<s>",
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"eos_token": "</s>",
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"pad_token": "</s>",
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"unk_token": "<unk>"
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}
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checkpoint-1000/tokenization_internlm.py
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# coding=utf-8
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
|
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#
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# http://www.apache.org/licenses/LICENSE-2.0
|
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
|
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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# See the License for the specific language governing permissions and
|
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# limitations under the License.
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+
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"""Tokenization classes for IntermLM."""
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import os
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+
from shutil import copyfile
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from typing import Any, Dict, List, Optional, Tuple
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+
|
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import sentencepiece as spm
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+
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from transformers.tokenization_utils import PreTrainedTokenizer
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from transformers.utils import logging
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+
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+
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logger = logging.get_logger(__name__)
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+
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VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
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PRETRAINED_VOCAB_FILES_MAP = {}
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|
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|
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class InternLMTokenizer(PreTrainedTokenizer):
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"""
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Construct a InternLM tokenizer. Based on byte-level Byte-Pair-Encoding.
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Args:
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vocab_file (`str`):
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Path to the vocabulary file.
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
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model_input_names = ["input_ids", "attention_mask"]
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_auto_class = "AutoTokenizer"
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def __init__(
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self,
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vocab_file,
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unk_token="<unk>",
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bos_token="<s>",
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eos_token="</s>",
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pad_token="</s>",
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sp_model_kwargs: Optional[Dict[str, Any]] = None,
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add_bos_token=True,
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add_eos_token=False,
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decode_with_prefix_space=False,
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clean_up_tokenization_spaces=False,
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**kwargs,
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+
):
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+
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
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super().__init__(
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bos_token=bos_token,
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eos_token=eos_token,
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unk_token=unk_token,
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pad_token=pad_token,
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clean_up_tokenization_spaces=clean_up_tokenization_spaces,
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**kwargs,
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)
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self.vocab_file = vocab_file
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self.add_bos_token = add_bos_token
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self.add_eos_token = add_eos_token
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self.decode_with_prefix_space = decode_with_prefix_space
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.Load(vocab_file)
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self._no_prefix_space_tokens = None
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83 |
+
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84 |
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""" Initialisation"""
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85 |
+
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@property
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def no_prefix_space_tokens(self):
|
88 |
+
if self._no_prefix_space_tokens is None:
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+
vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
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self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
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return self._no_prefix_space_tokens
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+
|
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+
@property
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+
def vocab_size(self):
|
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"""Returns vocab size"""
|
96 |
+
return self.sp_model.get_piece_size()
|
97 |
+
|
98 |
+
@property
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99 |
+
def bos_token_id(self) -> Optional[int]:
|
100 |
+
return self.sp_model.bos_id()
|
101 |
+
|
102 |
+
@property
|
103 |
+
def eos_token_id(self) -> Optional[int]:
|
104 |
+
return self.sp_model.eos_id()
|
105 |
+
|
106 |
+
def get_vocab(self):
|
107 |
+
"""Returns vocab as a dict"""
|
108 |
+
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
|
109 |
+
vocab.update(self.added_tokens_encoder)
|
110 |
+
return vocab
|
111 |
+
|
112 |
+
def _tokenize(self, text):
|
113 |
+
"""Returns a tokenized string."""
|
114 |
+
return self.sp_model.encode(text, out_type=str)
|
115 |
+
|
116 |
+
def _convert_token_to_id(self, token):
|
117 |
+
"""Converts a token (str) in an id using the vocab."""
|
118 |
+
return self.sp_model.piece_to_id(token)
|
119 |
+
|
120 |
+
def _convert_id_to_token(self, index):
|
121 |
+
"""Converts an index (integer) in a token (str) using the vocab."""
|
122 |
+
token = self.sp_model.IdToPiece(index)
|
123 |
+
return token
|
124 |
+
|
125 |
+
def _maybe_add_prefix_space(self, tokens, decoded):
|
126 |
+
if tokens and tokens[0] not in self.no_prefix_space_tokens:
|
127 |
+
return " " + decoded
|
128 |
+
else:
|
129 |
+
return decoded
|
130 |
+
|
131 |
+
def convert_tokens_to_string(self, tokens):
|
132 |
+
"""Converts a sequence of tokens (string) in a single string."""
|
133 |
+
current_sub_tokens = []
|
134 |
+
out_string = ""
|
135 |
+
prev_is_special = False
|
136 |
+
for token in tokens:
|
137 |
+
# make sure that special tokens are not decoded using sentencepiece model
|
138 |
+
if token in self.all_special_tokens:
|
139 |
+
if not prev_is_special:
|
140 |
+
out_string += " "
|
141 |
+
out_string += self.sp_model.decode(current_sub_tokens) + token
|
142 |
+
prev_is_special = True
|
143 |
+
current_sub_tokens = []
|
144 |
+
else:
|
145 |
+
current_sub_tokens.append(token)
|
146 |
+
prev_is_special = False
|
147 |
+
out_string += self.sp_model.decode(current_sub_tokens)
|
148 |
+
out_string = self.clean_up_tokenization(out_string)
|
149 |
+
out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
|
150 |
+
return out_string[1:]
|
151 |
+
|
152 |
+
def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
153 |
+
"""
|
154 |
+
Save the vocabulary and special tokens file to a directory.
|
155 |
+
|
156 |
+
Args:
|
157 |
+
save_directory (`str`):
|
158 |
+
The directory in which to save the vocabulary.
|
159 |
+
|
160 |
+
Returns:
|
161 |
+
`Tuple(str)`: Paths to the files saved.
|
162 |
+
"""
|
163 |
+
if not os.path.isdir(save_directory):
|
164 |
+
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
165 |
+
return
|
166 |
+
out_vocab_file = os.path.join(
|
167 |
+
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
168 |
+
)
|
169 |
+
|
170 |
+
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
|
171 |
+
copyfile(self.vocab_file, out_vocab_file)
|
172 |
+
elif not os.path.isfile(self.vocab_file):
|
173 |
+
with open(out_vocab_file, "wb") as fi:
|
174 |
+
content_spiece_model = self.sp_model.serialized_model_proto()
|
175 |
+
fi.write(content_spiece_model)
|
176 |
+
|
177 |
+
return (out_vocab_file,)
|
178 |
+
|
179 |
+
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
|
180 |
+
if self.add_bos_token:
|
181 |
+
bos_token_ids = [self.bos_token_id]
|
182 |
+
else:
|
183 |
+
bos_token_ids = []
|
184 |
+
|
185 |
+
output = bos_token_ids + token_ids_0
|
186 |
+
|
187 |
+
if token_ids_1 is not None:
|
188 |
+
output = output + token_ids_1
|
189 |
+
|
190 |
+
if self.add_eos_token:
|
191 |
+
output = output + [self.eos_token_id]
|
192 |
+
|
193 |
+
return output
|
194 |
+
|
195 |
+
def get_special_tokens_mask(
|
196 |
+
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
|
197 |
+
) -> List[int]:
|
198 |
+
"""
|
199 |
+
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
|
200 |
+
special tokens using the tokenizer `prepare_for_model` method.
|
201 |
+
|
202 |
+
Args:
|
203 |
+
token_ids_0 (`List[int]`):
|
204 |
+
List of IDs.
|
205 |
+
token_ids_1 (`List[int]`, *optional*):
|
206 |
+
Optional second list of IDs for sequence pairs.
|
207 |
+
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
208 |
+
Whether or not the token list is already formatted with special tokens for the model.
|
209 |
+
|
210 |
+
Returns:
|
211 |
+
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
212 |
+
"""
|
213 |
+
if already_has_special_tokens:
|
214 |
+
return super().get_special_tokens_mask(
|
215 |
+
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
|
216 |
+
)
|
217 |
+
|
218 |
+
if token_ids_1 is None:
|
219 |
+
return [1] + ([0] * len(token_ids_0)) + [1]
|
220 |
+
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
|
221 |
+
|
222 |
+
def create_token_type_ids_from_sequences(
|
223 |
+
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
224 |
+
) -> List[int]:
|
225 |
+
"""
|
226 |
+
Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
|
227 |
+
use of token type ids, therefore a list of zeros is returned.
|
228 |
+
|
229 |
+
Args:
|
230 |
+
token_ids_0 (`List[int]`):
|
231 |
+
List of IDs.
|
232 |
+
token_ids_1 (`List[int]`, *optional*):
|
233 |
+
Optional second list of IDs for sequence pairs.
|
234 |
+
|
235 |
+
Returns:
|
236 |
+
`List[int]`: List of zeros.
|
237 |
+
"""
|
238 |
+
eos = [self.eos_token_id]
|
239 |
+
|
240 |
+
if token_ids_1 is None:
|
241 |
+
return len(token_ids_0 + eos) * [0]
|
242 |
+
return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
|
checkpoint-1000/tokenizer.model
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:aab622d98c98677a1a51f969e25765154487bf3e85c7819db105db2fcacba83f
|
3 |
+
size 1658691
|
checkpoint-1000/tokenizer_config.json
ADDED
@@ -0,0 +1,16 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"auto_map": {
|
3 |
+
"AutoTokenizer": [
|
4 |
+
"tokenization_internlm.InternLMTokenizer",
|
5 |
+
null
|
6 |
+
]
|
7 |
+
},
|
8 |
+
"bos_token": "<s>",
|
9 |
+
"clean_up_tokenization_spaces": false,
|
10 |
+
"eos_token": "</s>",
|
11 |
+
"model_max_length": 1000000000000000019884624838656,
|
12 |
+
"pad_token": "</s>",
|
13 |
+
"padding_side": "right",
|
14 |
+
"tokenizer_class": "InternLMTokenizer",
|
15 |
+
"unk_token": "<unk>"
|
16 |
+
}
|
checkpoint-1000/trainer_state.json
ADDED
@@ -0,0 +1,616 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
|
|
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|
|
|
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|
|
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|
|
|
|
|
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|
|
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|
|
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|
|
|
|
|
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|
|
|
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|
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|
|
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|
|
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|
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|
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|
|
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|
|
|
|
|
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|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
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|
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|
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|
|
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|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
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|
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|
|
|
|
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|
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|
|
|
|
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|
|
|
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|
|
|
|
|
|
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|
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|
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|
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|
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|
1 |
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{
|
2 |
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|
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|
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|
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|
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"is_hyper_param_search": false,
|
7 |
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"is_local_process_zero": true,
|
8 |
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"is_world_process_zero": true,
|
9 |
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"log_history": [
|
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{
|
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|
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|
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"loss": 1.7592,
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"step": 10
|
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},
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{
|
17 |
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|
18 |
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"learning_rate": 4.998938447446803e-05,
|
19 |
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"loss": 1.6546,
|
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"step": 20
|
21 |
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},
|
22 |
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{
|
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|
24 |
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|
25 |
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"step": 30
|
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|
28 |
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{
|
29 |
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"epoch": 0.04,
|
30 |
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"learning_rate": 4.9957546913022665e-05,
|
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"loss": 1.5245,
|
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"step": 40
|
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|
34 |
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{
|
35 |
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|
36 |
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"learning_rate": 4.993367761465736e-05,
|
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"loss": 1.5049,
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"step": 50
|
39 |
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|
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{
|
41 |
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|
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"step": 60
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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checkpoint-1000/training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:918a94b2b648985fdb03a840052eff79b0035343fd87ee2a0dc751e904fad0c4
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checkpoint-2000/README.md
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|
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---
|
2 |
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library_name: peft
|
3 |
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---
|
4 |
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## Training procedure
|
5 |
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|
6 |
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|
7 |
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The following `bitsandbytes` quantization config was used during training:
|
8 |
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- load_in_8bit: False
|
9 |
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- load_in_4bit: True
|
10 |
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- llm_int8_threshold: 6.0
|
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- llm_int8_skip_modules: None
|
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- llm_int8_enable_fp32_cpu_offload: False
|
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- llm_int8_has_fp16_weight: False
|
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- bnb_4bit_quant_type: nf4
|
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- bnb_4bit_use_double_quant: True
|
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+
- bnb_4bit_compute_dtype: float16
|
17 |
+
### Framework versions
|
18 |
+
|
19 |
+
|
20 |
+
- PEFT 0.4.0
|
checkpoint-2000/adapter_config.json
ADDED
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"peft_type": "LORA",
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"r": 8,
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"target_modules": [
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"q_proj",
|
18 |
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"v_proj"
|
19 |
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],
|
20 |
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"task_type": "CAUSAL_LM"
|
21 |
+
}
|
checkpoint-2000/adapter_model.bin
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version https://git-lfs.github.com/spec/v1
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size 39407885
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checkpoint-2000/optimizer.pt
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version https://git-lfs.github.com/spec/v1
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size 78784709
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checkpoint-2000/rng_state_0.pth
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version https://git-lfs.github.com/spec/v1
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size 18679
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checkpoint-2000/rng_state_1.pth
ADDED
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version https://git-lfs.github.com/spec/v1
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checkpoint-2000/scheduler.pt
ADDED
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|
|
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|
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+
version https://git-lfs.github.com/spec/v1
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size 627
|
checkpoint-2000/special_tokens_map.json
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"additional_special_tokens": [
|
3 |
+
"<eoa>"
|
4 |
+
],
|
5 |
+
"bos_token": "<s>",
|
6 |
+
"eos_token": "</s>",
|
7 |
+
"pad_token": "</s>",
|
8 |
+
"unk_token": "<unk>"
|
9 |
+
}
|
checkpoint-2000/tokenization_internlm.py
ADDED
@@ -0,0 +1,242 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
|
|
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|
|
|
|
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|
|
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|
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|
|
|
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|
|
|
|
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|
|
|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
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|
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|
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|
|
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|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
3 |
+
#
|
4 |
+
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
5 |
+
# and OPT implementations in this library. It has been modified from its
|
6 |
+
# original forms to accommodate minor architectural differences compared
|
7 |
+
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
8 |
+
#
|
9 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
10 |
+
# you may not use this file except in compliance with the License.
|
11 |
+
# You may obtain a copy of the License at
|
12 |
+
#
|
13 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
14 |
+
#
|
15 |
+
# Unless required by applicable law or agreed to in writing, software
|
16 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
17 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
18 |
+
# See the License for the specific language governing permissions and
|
19 |
+
# limitations under the License.
|
20 |
+
|
21 |
+
"""Tokenization classes for IntermLM."""
|
22 |
+
import os
|
23 |
+
from shutil import copyfile
|
24 |
+
from typing import Any, Dict, List, Optional, Tuple
|
25 |
+
|
26 |
+
import sentencepiece as spm
|
27 |
+
|
28 |
+
from transformers.tokenization_utils import PreTrainedTokenizer
|
29 |
+
from transformers.utils import logging
|
30 |
+
|
31 |
+
|
32 |
+
logger = logging.get_logger(__name__)
|
33 |
+
|
34 |
+
VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
|
35 |
+
|
36 |
+
PRETRAINED_VOCAB_FILES_MAP = {}
|
37 |
+
|
38 |
+
|
39 |
+
class InternLMTokenizer(PreTrainedTokenizer):
|
40 |
+
"""
|
41 |
+
Construct a InternLM tokenizer. Based on byte-level Byte-Pair-Encoding.
|
42 |
+
|
43 |
+
Args:
|
44 |
+
vocab_file (`str`):
|
45 |
+
Path to the vocabulary file.
|
46 |
+
"""
|
47 |
+
|
48 |
+
vocab_files_names = VOCAB_FILES_NAMES
|
49 |
+
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
|
50 |
+
model_input_names = ["input_ids", "attention_mask"]
|
51 |
+
_auto_class = "AutoTokenizer"
|
52 |
+
|
53 |
+
def __init__(
|
54 |
+
self,
|
55 |
+
vocab_file,
|
56 |
+
unk_token="<unk>",
|
57 |
+
bos_token="<s>",
|
58 |
+
eos_token="</s>",
|
59 |
+
pad_token="</s>",
|
60 |
+
sp_model_kwargs: Optional[Dict[str, Any]] = None,
|
61 |
+
add_bos_token=True,
|
62 |
+
add_eos_token=False,
|
63 |
+
decode_with_prefix_space=False,
|
64 |
+
clean_up_tokenization_spaces=False,
|
65 |
+
**kwargs,
|
66 |
+
):
|
67 |
+
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
|
68 |
+
super().__init__(
|
69 |
+
bos_token=bos_token,
|
70 |
+
eos_token=eos_token,
|
71 |
+
unk_token=unk_token,
|
72 |
+
pad_token=pad_token,
|
73 |
+
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
74 |
+
**kwargs,
|
75 |
+
)
|
76 |
+
self.vocab_file = vocab_file
|
77 |
+
self.add_bos_token = add_bos_token
|
78 |
+
self.add_eos_token = add_eos_token
|
79 |
+
self.decode_with_prefix_space = decode_with_prefix_space
|
80 |
+
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
81 |
+
self.sp_model.Load(vocab_file)
|
82 |
+
self._no_prefix_space_tokens = None
|
83 |
+
|
84 |
+
""" Initialisation"""
|
85 |
+
|
86 |
+
@property
|
87 |
+
def no_prefix_space_tokens(self):
|
88 |
+
if self._no_prefix_space_tokens is None:
|
89 |
+
vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
|
90 |
+
self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
|
91 |
+
return self._no_prefix_space_tokens
|
92 |
+
|
93 |
+
@property
|
94 |
+
def vocab_size(self):
|
95 |
+
"""Returns vocab size"""
|
96 |
+
return self.sp_model.get_piece_size()
|
97 |
+
|
98 |
+
@property
|
99 |
+
def bos_token_id(self) -> Optional[int]:
|
100 |
+
return self.sp_model.bos_id()
|
101 |
+
|
102 |
+
@property
|
103 |
+
def eos_token_id(self) -> Optional[int]:
|
104 |
+
return self.sp_model.eos_id()
|
105 |
+
|
106 |
+
def get_vocab(self):
|
107 |
+
"""Returns vocab as a dict"""
|
108 |
+
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
|
109 |
+
vocab.update(self.added_tokens_encoder)
|
110 |
+
return vocab
|
111 |
+
|
112 |
+
def _tokenize(self, text):
|
113 |
+
"""Returns a tokenized string."""
|
114 |
+
return self.sp_model.encode(text, out_type=str)
|
115 |
+
|
116 |
+
def _convert_token_to_id(self, token):
|
117 |
+
"""Converts a token (str) in an id using the vocab."""
|
118 |
+
return self.sp_model.piece_to_id(token)
|
119 |
+
|
120 |
+
def _convert_id_to_token(self, index):
|
121 |
+
"""Converts an index (integer) in a token (str) using the vocab."""
|
122 |
+
token = self.sp_model.IdToPiece(index)
|
123 |
+
return token
|
124 |
+
|
125 |
+
def _maybe_add_prefix_space(self, tokens, decoded):
|
126 |
+
if tokens and tokens[0] not in self.no_prefix_space_tokens:
|
127 |
+
return " " + decoded
|
128 |
+
else:
|
129 |
+
return decoded
|
130 |
+
|
131 |
+
def convert_tokens_to_string(self, tokens):
|
132 |
+
"""Converts a sequence of tokens (string) in a single string."""
|
133 |
+
current_sub_tokens = []
|
134 |
+
out_string = ""
|
135 |
+
prev_is_special = False
|
136 |
+
for token in tokens:
|
137 |
+
# make sure that special tokens are not decoded using sentencepiece model
|
138 |
+
if token in self.all_special_tokens:
|
139 |
+
if not prev_is_special:
|
140 |
+
out_string += " "
|
141 |
+
out_string += self.sp_model.decode(current_sub_tokens) + token
|
142 |
+
prev_is_special = True
|
143 |
+
current_sub_tokens = []
|
144 |
+
else:
|
145 |
+
current_sub_tokens.append(token)
|
146 |
+
prev_is_special = False
|
147 |
+
out_string += self.sp_model.decode(current_sub_tokens)
|
148 |
+
out_string = self.clean_up_tokenization(out_string)
|
149 |
+
out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
|
150 |
+
return out_string[1:]
|
151 |
+
|
152 |
+
def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
153 |
+
"""
|
154 |
+
Save the vocabulary and special tokens file to a directory.
|
155 |
+
|
156 |
+
Args:
|
157 |
+
save_directory (`str`):
|
158 |
+
The directory in which to save the vocabulary.
|
159 |
+
|
160 |
+
Returns:
|
161 |
+
`Tuple(str)`: Paths to the files saved.
|
162 |
+
"""
|
163 |
+
if not os.path.isdir(save_directory):
|
164 |
+
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
165 |
+
return
|
166 |
+
out_vocab_file = os.path.join(
|
167 |
+
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
168 |
+
)
|
169 |
+
|
170 |
+
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
|
171 |
+
copyfile(self.vocab_file, out_vocab_file)
|
172 |
+
elif not os.path.isfile(self.vocab_file):
|
173 |
+
with open(out_vocab_file, "wb") as fi:
|
174 |
+
content_spiece_model = self.sp_model.serialized_model_proto()
|
175 |
+
fi.write(content_spiece_model)
|
176 |
+
|
177 |
+
return (out_vocab_file,)
|
178 |
+
|
179 |
+
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
|
180 |
+
if self.add_bos_token:
|
181 |
+
bos_token_ids = [self.bos_token_id]
|
182 |
+
else:
|
183 |
+
bos_token_ids = []
|
184 |
+
|
185 |
+
output = bos_token_ids + token_ids_0
|
186 |
+
|
187 |
+
if token_ids_1 is not None:
|
188 |
+
output = output + token_ids_1
|
189 |
+
|
190 |
+
if self.add_eos_token:
|
191 |
+
output = output + [self.eos_token_id]
|
192 |
+
|
193 |
+
return output
|
194 |
+
|
195 |
+
def get_special_tokens_mask(
|
196 |
+
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
|
197 |
+
) -> List[int]:
|
198 |
+
"""
|
199 |
+
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
|
200 |
+
special tokens using the tokenizer `prepare_for_model` method.
|
201 |
+
|
202 |
+
Args:
|
203 |
+
token_ids_0 (`List[int]`):
|
204 |
+
List of IDs.
|
205 |
+
token_ids_1 (`List[int]`, *optional*):
|
206 |
+
Optional second list of IDs for sequence pairs.
|
207 |
+
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
208 |
+
Whether or not the token list is already formatted with special tokens for the model.
|
209 |
+
|
210 |
+
Returns:
|
211 |
+
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
212 |
+
"""
|
213 |
+
if already_has_special_tokens:
|
214 |
+
return super().get_special_tokens_mask(
|
215 |
+
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
|
216 |
+
)
|
217 |
+
|
218 |
+
if token_ids_1 is None:
|
219 |
+
return [1] + ([0] * len(token_ids_0)) + [1]
|
220 |
+
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
|
221 |
+
|
222 |
+
def create_token_type_ids_from_sequences(
|
223 |
+
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
224 |
+
) -> List[int]:
|
225 |
+
"""
|
226 |
+
Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
|
227 |
+
use of token type ids, therefore a list of zeros is returned.
|
228 |
+
|
229 |
+
Args:
|
230 |
+
token_ids_0 (`List[int]`):
|
231 |
+
List of IDs.
|
232 |
+
token_ids_1 (`List[int]`, *optional*):
|
233 |
+
Optional second list of IDs for sequence pairs.
|
234 |
+
|
235 |
+
Returns:
|
236 |
+
`List[int]`: List of zeros.
|
237 |
+
"""
|
238 |
+
eos = [self.eos_token_id]
|
239 |
+
|
240 |
+
if token_ids_1 is None:
|
241 |
+
return len(token_ids_0 + eos) * [0]
|
242 |
+
return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
|
checkpoint-2000/tokenizer.model
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:aab622d98c98677a1a51f969e25765154487bf3e85c7819db105db2fcacba83f
|
3 |
+
size 1658691
|
checkpoint-2000/tokenizer_config.json
ADDED
@@ -0,0 +1,16 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"auto_map": {
|
3 |
+
"AutoTokenizer": [
|
4 |
+
"tokenization_internlm.InternLMTokenizer",
|
5 |
+
null
|
6 |
+
]
|
7 |
+
},
|
8 |
+
"bos_token": "<s>",
|
9 |
+
"clean_up_tokenization_spaces": false,
|
10 |
+
"eos_token": "</s>",
|
11 |
+
"model_max_length": 1000000000000000019884624838656,
|
12 |
+
"pad_token": "</s>",
|
13 |
+
"padding_side": "right",
|
14 |
+
"tokenizer_class": "InternLMTokenizer",
|
15 |
+
"unk_token": "<unk>"
|
16 |
+
}
|
checkpoint-2000/trainer_state.json
ADDED
@@ -0,0 +1,1216 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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"step": 1920
|
1161 |
+
},
|
1162 |
+
{
|
1163 |
+
"epoch": 1.79,
|
1164 |
+
"learning_rate": 1.3433857233289714e-06,
|
1165 |
+
"loss": 1.3811,
|
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"step": 1930
|
1167 |
+
},
|
1168 |
+
{
|
1169 |
+
"epoch": 1.8,
|
1170 |
+
"learning_rate": 1.2280939395691859e-06,
|
1171 |
+
"loss": 1.384,
|
1172 |
+
"step": 1940
|
1173 |
+
},
|
1174 |
+
{
|
1175 |
+
"epoch": 1.81,
|
1176 |
+
"learning_rate": 1.1178494492365465e-06,
|
1177 |
+
"loss": 1.3821,
|
1178 |
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"step": 1950
|
1179 |
+
},
|
1180 |
+
{
|
1181 |
+
"epoch": 1.82,
|
1182 |
+
"learning_rate": 1.0126756596375686e-06,
|
1183 |
+
"loss": 1.3978,
|
1184 |
+
"step": 1960
|
1185 |
+
},
|
1186 |
+
{
|
1187 |
+
"epoch": 1.83,
|
1188 |
+
"learning_rate": 9.125949014585383e-07,
|
1189 |
+
"loss": 1.3724,
|
1190 |
+
"step": 1970
|
1191 |
+
},
|
1192 |
+
{
|
1193 |
+
"epoch": 1.84,
|
1194 |
+
"learning_rate": 8.176284240242638e-07,
|
1195 |
+
"loss": 1.3851,
|
1196 |
+
"step": 1980
|
1197 |
+
},
|
1198 |
+
{
|
1199 |
+
"epoch": 1.85,
|
1200 |
+
"learning_rate": 7.277963907863478e-07,
|
1201 |
+
"loss": 1.3793,
|
1202 |
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"step": 1990
|
1203 |
+
},
|
1204 |
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{
|
1205 |
+
"epoch": 1.85,
|
1206 |
+
"learning_rate": 6.431178750420513e-07,
|
1207 |
+
"loss": 1.3946,
|
1208 |
+
"step": 2000
|
1209 |
+
}
|
1210 |
+
],
|
1211 |
+
"max_steps": 2156,
|
1212 |
+
"num_train_epochs": 2,
|
1213 |
+
"total_flos": 4.3007034481621074e+18,
|
1214 |
+
"trial_name": null,
|
1215 |
+
"trial_params": null
|
1216 |
+
}
|
checkpoint-2000/training_args.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:918a94b2b648985fdb03a840052eff79b0035343fd87ee2a0dc751e904fad0c4
|
3 |
+
size 4091
|
special_tokens_map.json
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"additional_special_tokens": [
|
3 |
+
"<eoa>"
|
4 |
+
],
|
5 |
+
"bos_token": "<s>",
|
6 |
+
"eos_token": "</s>",
|
7 |
+
"pad_token": "</s>",
|
8 |
+
"unk_token": "<unk>"
|
9 |
+
}
|
tokenization_internlm.py
ADDED
@@ -0,0 +1,242 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
3 |
+
#
|
4 |
+
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
5 |
+
# and OPT implementations in this library. It has been modified from its
|
6 |
+
# original forms to accommodate minor architectural differences compared
|
7 |
+
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
8 |
+
#
|
9 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
10 |
+
# you may not use this file except in compliance with the License.
|
11 |
+
# You may obtain a copy of the License at
|
12 |
+
#
|
13 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
14 |
+
#
|
15 |
+
# Unless required by applicable law or agreed to in writing, software
|
16 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
17 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
18 |
+
# See the License for the specific language governing permissions and
|
19 |
+
# limitations under the License.
|
20 |
+
|
21 |
+
"""Tokenization classes for IntermLM."""
|
22 |
+
import os
|
23 |
+
from shutil import copyfile
|
24 |
+
from typing import Any, Dict, List, Optional, Tuple
|
25 |
+
|
26 |
+
import sentencepiece as spm
|
27 |
+
|
28 |
+
from transformers.tokenization_utils import PreTrainedTokenizer
|
29 |
+
from transformers.utils import logging
|
30 |
+
|
31 |
+
|
32 |
+
logger = logging.get_logger(__name__)
|
33 |
+
|
34 |
+
VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
|
35 |
+
|
36 |
+
PRETRAINED_VOCAB_FILES_MAP = {}
|
37 |
+
|
38 |
+
|
39 |
+
class InternLMTokenizer(PreTrainedTokenizer):
|
40 |
+
"""
|
41 |
+
Construct a InternLM tokenizer. Based on byte-level Byte-Pair-Encoding.
|
42 |
+
|
43 |
+
Args:
|
44 |
+
vocab_file (`str`):
|
45 |
+
Path to the vocabulary file.
|
46 |
+
"""
|
47 |
+
|
48 |
+
vocab_files_names = VOCAB_FILES_NAMES
|
49 |
+
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
|
50 |
+
model_input_names = ["input_ids", "attention_mask"]
|
51 |
+
_auto_class = "AutoTokenizer"
|
52 |
+
|
53 |
+
def __init__(
|
54 |
+
self,
|
55 |
+
vocab_file,
|
56 |
+
unk_token="<unk>",
|
57 |
+
bos_token="<s>",
|
58 |
+
eos_token="</s>",
|
59 |
+
pad_token="</s>",
|
60 |
+
sp_model_kwargs: Optional[Dict[str, Any]] = None,
|
61 |
+
add_bos_token=True,
|
62 |
+
add_eos_token=False,
|
63 |
+
decode_with_prefix_space=False,
|
64 |
+
clean_up_tokenization_spaces=False,
|
65 |
+
**kwargs,
|
66 |
+
):
|
67 |
+
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
|
68 |
+
super().__init__(
|
69 |
+
bos_token=bos_token,
|
70 |
+
eos_token=eos_token,
|
71 |
+
unk_token=unk_token,
|
72 |
+
pad_token=pad_token,
|
73 |
+
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
74 |
+
**kwargs,
|
75 |
+
)
|
76 |
+
self.vocab_file = vocab_file
|
77 |
+
self.add_bos_token = add_bos_token
|
78 |
+
self.add_eos_token = add_eos_token
|
79 |
+
self.decode_with_prefix_space = decode_with_prefix_space
|
80 |
+
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
81 |
+
self.sp_model.Load(vocab_file)
|
82 |
+
self._no_prefix_space_tokens = None
|
83 |
+
|
84 |
+
""" Initialisation"""
|
85 |
+
|
86 |
+
@property
|
87 |
+
def no_prefix_space_tokens(self):
|
88 |
+
if self._no_prefix_space_tokens is None:
|
89 |
+
vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
|
90 |
+
self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
|
91 |
+
return self._no_prefix_space_tokens
|
92 |
+
|
93 |
+
@property
|
94 |
+
def vocab_size(self):
|
95 |
+
"""Returns vocab size"""
|
96 |
+
return self.sp_model.get_piece_size()
|
97 |
+
|
98 |
+
@property
|
99 |
+
def bos_token_id(self) -> Optional[int]:
|
100 |
+
return self.sp_model.bos_id()
|
101 |
+
|
102 |
+
@property
|
103 |
+
def eos_token_id(self) -> Optional[int]:
|
104 |
+
return self.sp_model.eos_id()
|
105 |
+
|
106 |
+
def get_vocab(self):
|
107 |
+
"""Returns vocab as a dict"""
|
108 |
+
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
|
109 |
+
vocab.update(self.added_tokens_encoder)
|
110 |
+
return vocab
|
111 |
+
|
112 |
+
def _tokenize(self, text):
|
113 |
+
"""Returns a tokenized string."""
|
114 |
+
return self.sp_model.encode(text, out_type=str)
|
115 |
+
|
116 |
+
def _convert_token_to_id(self, token):
|
117 |
+
"""Converts a token (str) in an id using the vocab."""
|
118 |
+
return self.sp_model.piece_to_id(token)
|
119 |
+
|
120 |
+
def _convert_id_to_token(self, index):
|
121 |
+
"""Converts an index (integer) in a token (str) using the vocab."""
|
122 |
+
token = self.sp_model.IdToPiece(index)
|
123 |
+
return token
|
124 |
+
|
125 |
+
def _maybe_add_prefix_space(self, tokens, decoded):
|
126 |
+
if tokens and tokens[0] not in self.no_prefix_space_tokens:
|
127 |
+
return " " + decoded
|
128 |
+
else:
|
129 |
+
return decoded
|
130 |
+
|
131 |
+
def convert_tokens_to_string(self, tokens):
|
132 |
+
"""Converts a sequence of tokens (string) in a single string."""
|
133 |
+
current_sub_tokens = []
|
134 |
+
out_string = ""
|
135 |
+
prev_is_special = False
|
136 |
+
for token in tokens:
|
137 |
+
# make sure that special tokens are not decoded using sentencepiece model
|
138 |
+
if token in self.all_special_tokens:
|
139 |
+
if not prev_is_special:
|
140 |
+
out_string += " "
|
141 |
+
out_string += self.sp_model.decode(current_sub_tokens) + token
|
142 |
+
prev_is_special = True
|
143 |
+
current_sub_tokens = []
|
144 |
+
else:
|
145 |
+
current_sub_tokens.append(token)
|
146 |
+
prev_is_special = False
|
147 |
+
out_string += self.sp_model.decode(current_sub_tokens)
|
148 |
+
out_string = self.clean_up_tokenization(out_string)
|
149 |
+
out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
|
150 |
+
return out_string[1:]
|
151 |
+
|
152 |
+
def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
153 |
+
"""
|
154 |
+
Save the vocabulary and special tokens file to a directory.
|
155 |
+
|
156 |
+
Args:
|
157 |
+
save_directory (`str`):
|
158 |
+
The directory in which to save the vocabulary.
|
159 |
+
|
160 |
+
Returns:
|
161 |
+
`Tuple(str)`: Paths to the files saved.
|
162 |
+
"""
|
163 |
+
if not os.path.isdir(save_directory):
|
164 |
+
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
165 |
+
return
|
166 |
+
out_vocab_file = os.path.join(
|
167 |
+
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
168 |
+
)
|
169 |
+
|
170 |
+
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
|
171 |
+
copyfile(self.vocab_file, out_vocab_file)
|
172 |
+
elif not os.path.isfile(self.vocab_file):
|
173 |
+
with open(out_vocab_file, "wb") as fi:
|
174 |
+
content_spiece_model = self.sp_model.serialized_model_proto()
|
175 |
+
fi.write(content_spiece_model)
|
176 |
+
|
177 |
+
return (out_vocab_file,)
|
178 |
+
|
179 |
+
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
|
180 |
+
if self.add_bos_token:
|
181 |
+
bos_token_ids = [self.bos_token_id]
|
182 |
+
else:
|
183 |
+
bos_token_ids = []
|
184 |
+
|
185 |
+
output = bos_token_ids + token_ids_0
|
186 |
+
|
187 |
+
if token_ids_1 is not None:
|
188 |
+
output = output + token_ids_1
|
189 |
+
|
190 |
+
if self.add_eos_token:
|
191 |
+
output = output + [self.eos_token_id]
|
192 |
+
|
193 |
+
return output
|
194 |
+
|
195 |
+
def get_special_tokens_mask(
|
196 |
+
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
|
197 |
+
) -> List[int]:
|
198 |
+
"""
|
199 |
+
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
|
200 |
+
special tokens using the tokenizer `prepare_for_model` method.
|
201 |
+
|
202 |
+
Args:
|
203 |
+
token_ids_0 (`List[int]`):
|
204 |
+
List of IDs.
|
205 |
+
token_ids_1 (`List[int]`, *optional*):
|
206 |
+
Optional second list of IDs for sequence pairs.
|
207 |
+
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
208 |
+
Whether or not the token list is already formatted with special tokens for the model.
|
209 |
+
|
210 |
+
Returns:
|
211 |
+
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
212 |
+
"""
|
213 |
+
if already_has_special_tokens:
|
214 |
+
return super().get_special_tokens_mask(
|
215 |
+
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
|
216 |
+
)
|
217 |
+
|
218 |
+
if token_ids_1 is None:
|
219 |
+
return [1] + ([0] * len(token_ids_0)) + [1]
|
220 |
+
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
|
221 |
+
|
222 |
+
def create_token_type_ids_from_sequences(
|
223 |
+
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
224 |
+
) -> List[int]:
|
225 |
+
"""
|
226 |
+
Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
|
227 |
+
use of token type ids, therefore a list of zeros is returned.
|
228 |
+
|
229 |
+
Args:
|
230 |
+
token_ids_0 (`List[int]`):
|
231 |
+
List of IDs.
|
232 |
+
token_ids_1 (`List[int]`, *optional*):
|
233 |
+
Optional second list of IDs for sequence pairs.
|
234 |
+
|
235 |
+
Returns:
|
236 |
+
`List[int]`: List of zeros.
|
237 |
+
"""
|
238 |
+
eos = [self.eos_token_id]
|
239 |
+
|
240 |
+
if token_ids_1 is None:
|
241 |
+
return len(token_ids_0 + eos) * [0]
|
242 |
+
return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
|
tokenizer.model
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:aab622d98c98677a1a51f969e25765154487bf3e85c7819db105db2fcacba83f
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size 1658691
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tokenizer_config.json
ADDED
@@ -0,0 +1,16 @@
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{
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"auto_map": {
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"AutoTokenizer": [
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"tokenization_internlm.InternLMTokenizer",
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null
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]
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},
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "</s>",
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "</s>",
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"padding_side": "right",
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"tokenizer_class": "InternLMTokenizer",
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"unk_token": "<unk>"
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}
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train_results.json
ADDED
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{
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"train_steps_per_second": 0.042
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}
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trainer_log.jsonl
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|
trainer_state.json
ADDED
@@ -0,0 +1,1315 @@
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