Upload 8 files
Browse files- README.md +40 -0
- adapter_config.json +31 -0
- adapter_model.safetensors +3 -0
- config.json +49 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +23 -0
- tokenizer.json +0 -0
- tokenizer_config.json +123 -0
README.md
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---
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license: apache-2.0
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---
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---
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license: apache-2.0
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tags:
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- trl
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- ppo
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- transformers
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- reinforcement-learning
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---
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# TRL Model
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This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
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guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
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## Usage
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To use this model for inference, first install the TRL library:
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```bash
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python -m pip install trl
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```
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You can then generate text as follows:
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```python
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from transformers import pipeline
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generator = pipeline("text-generation", model="deepaknh/rlhf-falcon_deepak_v1")
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outputs = generator("Hello, my llama is cute")
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```
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If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
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```python
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from transformers import AutoTokenizer
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from trl import AutoModelForCausalLMWithValueHead
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tokenizer = AutoTokenizer.from_pretrained("deepaknh/rlhf-falcon_deepak_v1")
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model = AutoModelForCausalLMWithValueHead.from_pretrained("deepaknh/rlhf-falcon_deepak_v1")
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inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
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outputs = model(**inputs, labels=inputs["input_ids"])
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```
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adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "vilsonrodrigues/falcon-7b-instruct-sharded",
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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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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 512,
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"lora_dropout": 0.1,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 256,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"query_key_value",
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"dense",
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"dense_h_to_4h",
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"dense_4h_to_h"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"use_rslora": false
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}
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:42f92f64b4f1c0ae5131f5e2980fd5ee1c44fb40ec6abc5a8e32cd355e2029cd
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size 2088800520
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config.json
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{
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"accelerator_kwargs": {},
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"adap_kl_ctrl": true,
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"backward_batch_size": 4,
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"batch_size": 32,
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"cliprange": 0.2,
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"cliprange_value": 0.2,
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"compare_steps": 1,
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"early_stopping": false,
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"exp_name": "ipykernel_launcher",
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"forward_batch_size": null,
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"gamma": 1,
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"global_backward_batch_size": 4,
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"global_batch_size": 32,
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"gradient_accumulation_steps": 4,
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"horizon": 10000,
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"init_kl_coef": 0.2,
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"is_encoder_decoder": false,
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"is_peft_model": true,
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"kl_penalty": "kl",
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"lam": 0.95,
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"learning_rate": 2.5e-06,
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"log_with": "wandb",
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"max_grad_norm": null,
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"mini_batch_size": 1,
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"model_name": "deepaknh/falcon7B_FineTuning_Experiment2_QLORA_7perParam",
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"optimize_cuda_cache": true,
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"optimize_device_cache": false,
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"ppo_epochs": 20,
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"project_kwargs": {},
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"push_to_hub_if_best_kwargs": {},
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"query_dataset": "imdb",
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"ratio_threshold": 10.0,
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"remove_unused_columns": false,
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"reward_model": "sentiment-analysis:lvwerra/distilbert-imdb",
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"score_clip": null,
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"seed": 0,
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"steps": 1000,
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"target": 6,
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"target_kl": 0.1,
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"task_name": null,
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"tracker_kwargs": {},
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"tracker_project_name": "trl",
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"use_score_norm": false,
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"use_score_scaling": false,
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"vf_coef": 0.1,
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"whiten_rewards": false,
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"world_size": 1
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:b70550b9d090adf02d5bfa44f81ea2a021cdd2d3c3b508d71e5d92ea9dce9b56
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size 19708
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special_tokens_map.json
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{
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"additional_special_tokens": [
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">>TITLE<<",
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">>ABSTRACT<<",
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">>INTRODUCTION<<",
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">>SUMMARY<<",
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">>COMMENT<<",
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">>ANSWER<<",
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">>QUESTION<<",
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">>DOMAIN<<",
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">>PREFIX<<",
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">>SUFFIX<<",
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">>MIDDLE<<"
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],
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"eos_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": "<|endoftext|>"
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}
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tokenizer.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"added_tokens_decoder": {
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"0": {
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"content": ">>TITLE<<",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": ">>ABSTRACT<<",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"2": {
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"content": ">>INTRODUCTION<<",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"3": {
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"content": ">>SUMMARY<<",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"4": {
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"content": ">>COMMENT<<",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"5": {
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"content": ">>ANSWER<<",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"6": {
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"content": ">>QUESTION<<",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"7": {
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"content": ">>DOMAIN<<",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"8": {
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"content": ">>PREFIX<<",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"9": {
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"content": ">>SUFFIX<<",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"10": {
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"content": ">>MIDDLE<<",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"11": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"additional_special_tokens": [
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">>TITLE<<",
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">>ABSTRACT<<",
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">>INTRODUCTION<<",
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">>SUMMARY<<",
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">>COMMENT<<",
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">>ANSWER<<",
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">>QUESTION<<",
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">>DOMAIN<<",
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">>PREFIX<<",
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">>SUFFIX<<",
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">>MIDDLE<<"
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],
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|endoftext|>",
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"model_input_names": [
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"input_ids",
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"attention_mask"
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],
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"model_max_length": 2048,
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"pad_token": "<|endoftext|>",
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"tokenizer_class": "PreTrainedTokenizerFast"
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}
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