Upload folder using huggingface_hub (#1)
Browse files- 0f61f09fb1e79d91b7e5360d6a8fbce506e1e38bc2b433e8bf357feabe8dc8ff (acc15435290edc26384dfe373889bc15f58703d3)
- README.md +85 -0
- config.json +45 -0
- generation_config.json +6 -0
- model.safetensors +3 -0
- smash_config.json +31 -0
- special_tokens_map.json +23 -0
- tokenizer.json +0 -0
- tokenizer_config.json +847 -0
README.md
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---
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thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
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base_model: croissantllm/CroissantLLMBase
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metrics:
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- memory_disk
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- memory_inference
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- inference_latency
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- inference_throughput
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- inference_CO2_emissions
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- inference_energy_consumption
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tags:
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- pruna-ai
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---
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<!-- header start -->
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<!-- 200823 -->
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<div style="width: auto; margin-left: auto; margin-right: auto">
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<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
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<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</a>
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</div>
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<!-- header end -->
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[![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI)
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[![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI)
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[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
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[![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/CP4VSgck)
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# Simply make AI models cheaper, smaller, faster, and greener!
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- Give a thumbs up if you like this model!
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- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
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- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
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- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
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- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
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## Results
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![image info](./plots.png)
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**Frequently Asked Questions**
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- ***How does the compression work?*** The model is compressed with llm-int8.
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- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
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- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
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- ***What is the model format?*** We use safetensors.
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- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
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- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
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- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
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- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
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- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
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## Setup
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You can run the smashed model with these steps:
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0. Check requirements from the original repo croissantllm/CroissantLLMBase installed. In particular, check python, cuda, and transformers versions.
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1. Make sure that you have installed quantization related packages.
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```bash
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pip install transformers accelerate bitsandbytes>0.37.0
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```
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2. Load & run the model.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("PrunaAI/croissantllm-CroissantLLMBase-bnb-4bit-smashed", trust_remote_code=True, device_map='auto')
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tokenizer = AutoTokenizer.from_pretrained("croissantllm/CroissantLLMBase")
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input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
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outputs = model.generate(input_ids, max_new_tokens=216)
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tokenizer.decode(outputs[0])
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```
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## Configurations
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The configuration info are in `smash_config.json`.
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## Credits & License
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The license of the smashed model follows the license of the original model. Please check the license of the original model croissantllm/CroissantLLMBase before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
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## Want to compress other models?
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- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
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- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
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config.json
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{
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"_name_or_path": "/ceph/hdd/staff/charpent/.cache/models6ombdl_jl8ntoxyj",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 5504,
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"max_position_embeddings": 2048,
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"model_type": "llama",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"num_key_value_heads": 16,
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"pretraining_tp": 1,
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"quantization_config": {
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"_load_in_4bit": true,
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"_load_in_8bit": false,
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"bnb_4bit_compute_dtype": "bfloat16",
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"bnb_4bit_quant_storage": "uint8",
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"bnb_4bit_quant_type": "fp4",
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"bnb_4bit_use_double_quant": false,
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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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"llm_int8_skip_modules": [
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"lm_head"
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],
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"llm_int8_threshold": 6.0,
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"load_in_4bit": true,
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"load_in_8bit": false,
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"quant_method": "bitsandbytes"
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},
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.40.0",
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"use_cache": true,
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"vocab_size": 32000
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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": 1,
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"eos_token_id": 2,
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"transformers_version": "4.40.0"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:bbaf8614c6ed2ba0beceadec6af31f9843c73123fffe03cfd8e258753ca33692
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size 945470792
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smash_config.json
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{
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"api_key": null,
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"verify_url": "http://johnrachwan.pythonanywhere.com",
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"smash_config": {
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"pruners": "None",
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"pruning_ratio": 0.0,
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"factorizers": "None",
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"quantizers": "['llm-int8']",
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"weight_quantization_bits": 4,
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"output_deviation": 0.005,
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"compilers": "None",
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"static_batch": true,
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"static_shape": true,
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"controlnet": "None",
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"unet_dim": 4,
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"device": "cuda",
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"cache_dir": "/ceph/hdd/staff/charpent/.cache/models6ombdl_j",
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"batch_size": 1,
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"model_name": "croissantllm/CroissantLLMBase",
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"task": "text_text_generation",
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"max_batch_size": 1,
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"qtype_weight": "torch.qint8",
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"qtype_activation": "torch.quint8",
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"qobserver": "<class 'torch.ao.quantization.observer.MinMaxObserver'>",
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"qscheme": "torch.per_tensor_symmetric",
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"qconfig": "x86",
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"group_size": 128,
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"damp_percent": 0.1,
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"save_load_fn": "bitsandbytes"
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}
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}
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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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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"eos_token": {
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"content": "</s>",
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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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"unk_token": {
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"content": "<unk>",
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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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}
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tokenizer.json
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tokenizer_config.json
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|
1 |
+
{
|
2 |
+
"add_bos_token": true,
|
3 |
+
"add_eos_token": false,
|
4 |
+
"added_tokens_decoder": {
|
5 |
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"0": {
|
6 |
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"content": "<unk>",
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7 |
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8 |
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|
9 |
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10 |
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|
11 |
+
"special": true
|
12 |
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},
|
13 |
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"1": {
|
14 |
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|
15 |
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|
16 |
+
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|
17 |
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|
18 |
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|
19 |
+
"special": true
|
20 |
+
},
|
21 |
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"2": {
|
22 |
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"content": "</s>",
|
23 |
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"lstrip": false,
|
24 |
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|
25 |
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"rstrip": false,
|
26 |
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|
27 |
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"special": true
|
28 |
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},
|
29 |
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"3": {
|
30 |
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|
31 |
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|
32 |
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|
33 |
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|
34 |
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|
35 |
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|
36 |
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},
|
37 |
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"4": {
|
38 |
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|
39 |
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|
40 |
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|
41 |
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|
42 |
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|
43 |
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"special": true
|
44 |
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|
45 |
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"5": {
|
46 |
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|
47 |
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|
48 |
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|
49 |
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|
50 |
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|
51 |
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|
52 |
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|
53 |
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"6": {
|
54 |
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|
55 |
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|
56 |
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|
57 |
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|
58 |
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|
59 |
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|
60 |
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|
61 |
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"7": {
|
62 |
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|
63 |
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|
64 |
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|
65 |
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|
66 |
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67 |
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|
68 |
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|
69 |
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"8": {
|
70 |
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|
71 |
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|
72 |
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|
73 |
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|
74 |
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75 |
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|
76 |
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|
77 |
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"9": {
|
78 |
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|
79 |
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|
80 |
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|
81 |
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|
82 |
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|
83 |
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|
84 |
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|
85 |
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"10": {
|
86 |
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|
87 |
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|
88 |
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|
89 |
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|
90 |
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|
91 |
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|
92 |
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|
93 |
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|
94 |
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|
95 |
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|
96 |
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|
97 |
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|
98 |
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|
99 |
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|
100 |
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|
101 |
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"12": {
|
102 |
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|
103 |
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|
104 |
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|
105 |
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|
106 |
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|
107 |
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|
108 |
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109 |
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"13": {
|
110 |
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|
111 |
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|
112 |
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|
113 |
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|
114 |
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|
115 |
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|
116 |
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|
117 |
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|
118 |
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|
119 |
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120 |
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|
121 |
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|
122 |
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123 |
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124 |
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125 |
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|
126 |
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|
127 |
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128 |
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|
129 |
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130 |
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131 |
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132 |
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133 |
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|
134 |
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|
135 |
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136 |
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|
137 |
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|
138 |
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139 |
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140 |
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141 |
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|
142 |
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|
143 |
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|
144 |
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|
145 |
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|
146 |
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147 |
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148 |
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149 |
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|
150 |
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|
151 |
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152 |
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153 |
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154 |
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155 |
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|
156 |
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157 |
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|
158 |
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|
159 |
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160 |
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161 |
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162 |
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163 |
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|
164 |
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},
|
165 |
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|
166 |
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|
167 |
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|
168 |
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|
169 |
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|
170 |
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|
171 |
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|
172 |
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},
|
173 |
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|
174 |
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|
175 |
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|
176 |
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|
177 |
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|
178 |
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|
179 |
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|
180 |
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|
181 |
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|
182 |
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|
183 |
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|
184 |
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|
185 |
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|
186 |
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|
187 |
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|
188 |
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|
189 |
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|
190 |
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|
191 |
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|
192 |
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|
193 |
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|
194 |
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|
195 |
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|
196 |
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|
197 |
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|
198 |
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|
199 |
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|
200 |
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|
201 |
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|
202 |
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|
203 |
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|
204 |
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|
205 |
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|
206 |
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|
207 |
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|
208 |
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|
209 |
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|
210 |
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|
211 |
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|
212 |
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|
213 |
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|
214 |
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|
215 |
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|
216 |
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|
217 |
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|
218 |
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|
219 |
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|
220 |
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|
221 |
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|
222 |
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|
223 |
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|
224 |
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|
225 |
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|
226 |
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|
227 |
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|
228 |
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|
229 |
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|
230 |
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|
231 |
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|
232 |
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233 |
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234 |
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|
235 |
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|
236 |
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|
237 |
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|
238 |
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|
239 |
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|
240 |
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|
241 |
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242 |
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|
243 |
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|
244 |
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|
245 |
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|
246 |
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|
247 |
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248 |
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|
249 |
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250 |
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251 |
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252 |
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253 |
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|
254 |
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255 |
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256 |
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260 |
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261 |
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|
262 |
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|
263 |
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264 |
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268 |
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|
270 |
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271 |
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272 |
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273 |
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274 |
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275 |
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276 |
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277 |
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|
278 |
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|
279 |
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280 |
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283 |
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284 |
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285 |
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