Add HuggingFaceH4/starcoderplus-ift-v4.1 checkpoint
Browse files- README.md +66 -0
- added_tokens.json +6 -0
- all_results.json +14 -0
- config.json +39 -0
- dialogue_template.json +12 -0
- eval_results.json +9 -0
- generation_config.json +6 -0
- handler.py +49 -0
- merges.txt +0 -0
- pytorch_model-00001-of-00004.bin +3 -0
- pytorch_model-00002-of-00004.bin +3 -0
- pytorch_model-00003-of-00004.bin +3 -0
- pytorch_model-00004-of-00004.bin +3 -0
- pytorch_model.bin.index.json +492 -0
- requirements.txt +5 -0
- runs/Jun03_19-41-09_ip-26-0-150-31/1685821571.9023504/events.out.tfevents.1685821571.ip-26-0-150-31.3019211.1 +3 -0
- runs/Jun03_19-41-09_ip-26-0-150-31/events.out.tfevents.1685821571.ip-26-0-150-31.3019211.0 +3 -0
- runs/Jun03_19-41-09_ip-26-0-150-31/events.out.tfevents.1685823884.ip-26-0-150-31.3019211.2 +3 -0
- special_tokens_map.json +11 -0
- tokenizer.json +0 -0
- tokenizer_config.json +31 -0
- train_results.json +8 -0
- trainer_state.json +145 -0
- training_args.bin +3 -0
- vocab.json +0 -0
README.md
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---
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tags:
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- generated_from_trainer
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model-index:
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- name: starcoderplus-ift-v41
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# starcoderplus-ift-v41
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This model is a fine-tuned version of [bigcode-data/starcoderplus](https://huggingface.co/bigcode-data/starcoderplus) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.4720
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 8
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 256
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- total_eval_batch_size: 32
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.03
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- num_epochs: 6
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 1.5321 | 0.98 | 15 | 1.2856 |
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| 1.2071 | 1.97 | 30 | 1.2620 |
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| 1.0162 | 2.95 | 45 | 1.2853 |
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| 0.8484 | 4.0 | 61 | 1.3274 |
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| 0.6981 | 4.98 | 76 | 1.3994 |
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| 0.5668 | 5.9 | 90 | 1.4720 |
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### Framework versions
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- Transformers 4.28.1
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- Pytorch 2.0.1+cu118
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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added_tokens.json
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{
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"<|assistant|>": 49154,
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"<|end|>": 49155,
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"<|system|>": 49152,
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"<|user|>": 49153
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}
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all_results.json
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{
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"epoch": 5.9,
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"eval_loss": 1.4719988107681274,
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"eval_runtime": 5.2039,
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"eval_samples": 202,
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"eval_samples_per_second": 38.817,
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"eval_steps_per_second": 1.345,
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"perplexity": 4.35793713301621,
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"train_loss": 0.9728257921006944,
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"train_runtime": 2307.7787,
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"train_samples": 3888,
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"train_samples_per_second": 10.108,
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"train_steps_per_second": 0.039
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}
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config.json
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{
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"_name_or_path": "/fsx/h4/checkpoints/starcoderplus-ift",
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"activation_function": "gelu",
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"architectures": [
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"GPTBigCodeForCausalLM"
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],
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"attention_softmax_in_fp32": true,
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"attn_pdrop": 0.1,
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"bos_token_id": 0,
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"embd_pdrop": 0.1,
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"eos_token_id": 0,
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"inference_runner": 0,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"max_batch_size": null,
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"max_sequence_length": null,
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"model_type": "gpt_bigcode",
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"multi_query": true,
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"n_embd": 6144,
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"n_head": 48,
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"n_inner": 24576,
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"n_layer": 40,
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"n_positions": 8192,
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"pad_key_length": true,
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"pre_allocate_kv_cache": false,
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"resid_pdrop": 0.1,
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"scale_attention_softmax_in_fp32": true,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"torch_dtype": "float16",
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"transformers_version": "4.28.1",
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"use_cache": true,
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"validate_runner_input": true,
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"vocab_size": 49156
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}
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dialogue_template.json
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{
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"system": "",
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"messages": null,
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"system_token": "<|system|>",
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"system_format": "standard",
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"user_token": "<|user|>",
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"assistant_token": "<|assistant|>",
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"end_token": "<|end|>",
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"mid_str": "\n",
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"end_str": "\n",
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"extra_end_text": ""
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}
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eval_results.json
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{
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"epoch": 5.9,
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"eval_loss": 1.4719988107681274,
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"eval_runtime": 5.2039,
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"eval_samples": 202,
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"eval_samples_per_second": 38.817,
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"eval_steps_per_second": 1.345,
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"perplexity": 4.35793713301621
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"eos_token_id": 0,
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"transformers_version": "4.28.1"
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}
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handler.py
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from typing import Any, Dict
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from peft import PeftConfig, PeftModel
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class EndpointHandler:
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def __init__(self, path=""):
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# load model and processor from path
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self.tokenizer = AutoTokenizer.from_pretrained(path)
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try:
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config = PeftConfig.from_pretrained(path)
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model = AutoModelForCausalLM.from_pretrained(
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config.base_model_name_or_path,
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return_dict=True,
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load_in_8bit=True,
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device_map="auto",
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torch_dtype=torch.float16,
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trust_remote_code=True,
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)
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model.resize_token_embeddings(len(self.tokenizer))
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model = PeftModel.from_pretrained(model, path)
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except Exception:
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model = AutoModelForCausalLM.from_pretrained(
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path, device_map="auto", load_in_8bit=True, torch_dtype=torch.float16, trust_remote_code=True
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)
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self.model = model
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
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# process input
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inputs = data.pop("inputs", data)
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parameters = data.pop("parameters", None)
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# preprocess
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inputs = self.tokenizer(inputs, return_tensors="pt").to(self.device)
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# pass inputs with all kwargs in data
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if parameters is not None:
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outputs = self.model.generate(**inputs, **parameters)
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else:
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outputs = self.model.generate(**inputs)
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# postprocess the prediction
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prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
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return [{"generated_text": prediction}]
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merges.txt
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pytorch_model-00001-of-00004.bin
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size 9958034229
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pytorch_model-00002-of-00004.bin
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size 9857383847
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pytorch_model-00003-of-00004.bin
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pytorch_model-00004-of-00004.bin
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pytorch_model.bin.index.json
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{
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tokenizer.json
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tokenizer_config.json
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training_args.bin
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vocab.json
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