Text Generation
Transformers
Safetensors
Portuguese
llama
analytics
analise-dados
portugues-BR
conversational
text-generation-inference
Instructions to use semantixai/Lloro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use semantixai/Lloro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="semantixai/Lloro") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("semantixai/Lloro") model = AutoModelForCausalLM.from_pretrained("semantixai/Lloro") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use semantixai/Lloro with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "semantixai/Lloro" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "semantixai/Lloro", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/semantixai/Lloro
- SGLang
How to use semantixai/Lloro with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "semantixai/Lloro" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "semantixai/Lloro", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "semantixai/Lloro" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "semantixai/Lloro", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use semantixai/Lloro with Docker Model Runner:
docker model run hf.co/semantixai/Lloro
Upload Model
#3
by fernandofinardi - opened
- special_tokens_map.json +1 -1
- tokenizer.json +8 -35
- tokenizer_config.json +7 -7
special_tokens_map.json
CHANGED
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"<s>",
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"</s>",
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"▁<PRE>",
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"▁<SUF>",
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"▁<MID>",
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"▁<EOT>",
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"▁<PRE>",
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"▁<MID>",
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"<s>",
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"</s>",
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"▁<PRE>",
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"▁<MID>",
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"▁<SUF>",
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"▁<EOT>",
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"▁<PRE>",
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"▁<MID>",
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tokenizer.json
CHANGED
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@@ -31,7 +31,7 @@
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"special": true
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},
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{
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"id":
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"content": "▁<PRE>",
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"single_word": false,
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"lstrip": true,
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"special": true
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},
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{
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"id":
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"content": "▁<
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"single_word": false,
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"lstrip": true,
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"rstrip": true,
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"special": true
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},
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{
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"id":
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"content": "▁<
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"single_word": false,
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"lstrip": true,
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"rstrip": true,
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"special": true
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},
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{
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"id":
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"content": "▁<EOT>",
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"single_word": false,
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"lstrip": true,
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"왕": 31996,
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"收": 31997,
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"弘": 31998,
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-
"给": 31999
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-
"▁<SU": 32000,
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"▁<SUF": 32001,
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-
"▁<PRE": 32002,
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"▁<M": 32003,
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| 32178 |
-
"▁<MID": 32004,
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"▁<E": 32005,
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"▁<EOT": 32006,
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"▁<PRE>": 32007,
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"▁<SUF>": 32008,
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"▁<MID>": 32009,
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"▁<EOT>": 32010,
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| 32185 |
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"▁<EOT><EOT>": 32011,
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| 32186 |
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"▁<EOT><EOT><EOT>": 32012,
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| 32187 |
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"▁<EOT><EOT><EOT><EOT>": 32013,
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| 32188 |
-
"▁<EOT><EOT><EOT><EOT><EOT>": 32014,
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| 32189 |
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"▁<EOT><EOT><EOT><EOT><EOT><EOT>": 32015
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},
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"merges": [
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"▁ t",
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"▁▁▁▁▁▁▁▁▁ ▁▁▁▁▁▁",
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"▁▁▁▁▁▁▁ ▁▁▁▁▁▁▁▁",
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"▁▁▁▁▁▁▁▁▁▁▁ ▁▁▁▁",
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"▁<SU F",
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"▁<M ID",
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"▁<PRE >",
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"▁<SUF >",
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]
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}
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}
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"special": true
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},
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{
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"id": 32000,
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"content": "▁<PRE>",
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"single_word": false,
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"lstrip": true,
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"special": true
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},
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{
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"id": 32001,
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"content": "▁<MID>",
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"single_word": false,
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"lstrip": true,
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"rstrip": true,
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"special": true
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},
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{
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"id": 32002,
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"content": "▁<SUF>",
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"single_word": false,
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"lstrip": true,
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"rstrip": true,
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"special": true
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},
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{
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"id": 32003,
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"content": "▁<EOT>",
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"single_word": false,
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"lstrip": true,
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"왕": 31996,
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"收": 31997,
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"弘": 31998,
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"给": 31999
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"merges": [
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"▁ t",
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"▁▁▁▁▁▁▁▁▁ ▁▁▁▁▁▁",
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"▁▁▁▁▁▁▁ ▁▁▁▁▁▁▁▁",
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"▁▁▁▁▁▁▁▁▁▁▁ ▁▁▁▁",
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"▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁▁"
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}
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tokenizer_config.json
CHANGED
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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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"content": "▁<PRE>",
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"lstrip": true,
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"normalized": 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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"content": "▁<
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"lstrip": true,
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"normalized": false,
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"rstrip": true,
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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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"lstrip": true,
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"normalized": false,
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"rstrip": true,
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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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"content": "▁<EOT>",
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"lstrip": true,
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"normalized": false,
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"<s>",
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"</s>",
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"▁<PRE>",
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-
"▁<SUF>",
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"▁<MID>",
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"▁<EOT>",
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"▁<PRE>",
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"▁<MID>",
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"single_word": false,
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"special": true
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},
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"32000": {
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"content": "▁<PRE>",
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"lstrip": true,
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"normalized": false,
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"single_word": false,
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"special": true
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},
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"32001": {
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"content": "▁<MID>",
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"lstrip": true,
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"normalized": false,
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"rstrip": true,
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"single_word": false,
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"special": true
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},
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"32002": {
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"content": "▁<SUF>",
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"lstrip": true,
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"normalized": false,
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"rstrip": true,
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"single_word": false,
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"special": true
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},
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"32003": {
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"content": "▁<EOT>",
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"lstrip": true,
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"normalized": false,
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"<s>",
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"</s>",
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"▁<EOT>",
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"▁<PRE>",
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"▁<MID>",
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