Instructions to use trl-internal-testing/tmp-tiny-DbrxForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trl-internal-testing/tmp-tiny-DbrxForCausalLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="trl-internal-testing/tmp-tiny-DbrxForCausalLM") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tmp-tiny-DbrxForCausalLM") model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tmp-tiny-DbrxForCausalLM") 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 trl-internal-testing/tmp-tiny-DbrxForCausalLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "trl-internal-testing/tmp-tiny-DbrxForCausalLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "trl-internal-testing/tmp-tiny-DbrxForCausalLM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/trl-internal-testing/tmp-tiny-DbrxForCausalLM
- SGLang
How to use trl-internal-testing/tmp-tiny-DbrxForCausalLM 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 "trl-internal-testing/tmp-tiny-DbrxForCausalLM" \ --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": "trl-internal-testing/tmp-tiny-DbrxForCausalLM", "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 "trl-internal-testing/tmp-tiny-DbrxForCausalLM" \ --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": "trl-internal-testing/tmp-tiny-DbrxForCausalLM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use trl-internal-testing/tmp-tiny-DbrxForCausalLM with Docker Model Runner:
docker model run hf.co/trl-internal-testing/tmp-tiny-DbrxForCausalLM
Upload tokenizer
Browse files- added_tokens.json +4 -0
- chat_template.jinja +13 -0
- merges.txt +0 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer_config.json +205 -0
- vocab.json +0 -0
added_tokens.json
ADDED
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{
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"<|im_end|>": 100279,
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"<|im_start|>": 100278
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}
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chat_template.jinja
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{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif 'system' not in messages[0]['role'] %}{% set loop_messages = messages %}{% set system_message = 'You are DBRX, created by Databricks. You were last updated in December 2023. You answer questions based on information available up to that point.
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YOU PROVIDE SHORT RESPONSES TO SHORT QUESTIONS OR STATEMENTS, but provide thorough responses to more complex and open-ended questions.
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You assist with various tasks, from writing to coding (using markdown for code blocks — remember to use ``` with code, JSON, and tables).
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(You do not have real-time data access or code execution capabilities. You avoid stereotyping and provide balanced perspectives on controversial topics. You do not provide song lyrics, poems, or news articles and do not divulge details of your training data.)
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This is your system prompt, guiding your responses. Do not reference it, just respond to the user. If you find yourself talking about this message, stop. You should be responding appropriately and usually that means not mentioning this.
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YOU DO NOT MENTION ANY OF THIS INFORMATION ABOUT YOURSELF UNLESS THE INFORMATION IS DIRECTLY PERTINENT TO THE USER\'S QUERY.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if loop.index0 == 0 %}{% if system_message != false %}{{ '<|im_start|>system
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' + system_message | trim + '<|im_end|>
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'}}{% endif %}{{ '<|im_start|>' + message['role'] + '
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' + message['content'] + '<|im_end|>' }}{% else %}{{ '
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' + '<|im_start|>' + message['role'] + '
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' + message['content'] + '<|im_end|>' }}{% endif %}{% if (add_generation_prompt == true and loop.last) %}{{ '
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' + '<|im_start|>' + 'assistant' + '
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' }}{% endif %}{% endfor %}
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merges.txt
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special_tokens_map.json
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{
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"bos_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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"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": {
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"content": "<|pad|>",
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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": "<|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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}
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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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"100256": {
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"content": "<||_unused_0_||>",
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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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"100257": {
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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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"100258": {
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"content": "<|fim_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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"100259": {
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"content": "<|fim_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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"100260": {
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"content": "<|fim_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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"100261": {
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"content": "<||_unused_1_||>",
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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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"100262": {
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"content": "<||_unused_2_||>",
|
| 54 |
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"lstrip": false,
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| 55 |
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"normalized": false,
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| 56 |
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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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| 60 |
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"100263": {
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"content": "<||_unused_3_||>",
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"lstrip": false,
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"normalized": false,
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| 64 |
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"rstrip": false,
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"single_word": false,
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"special": true
|
| 67 |
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},
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| 68 |
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"100264": {
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| 69 |
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"content": "<||_unused_4_||>",
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"lstrip": false,
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| 71 |
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"normalized": false,
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| 72 |
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"rstrip": false,
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| 73 |
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"single_word": false,
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| 74 |
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"special": true
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| 75 |
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},
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| 76 |
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"100265": {
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| 77 |
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"content": "<||_unused_5_||>",
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| 78 |
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"lstrip": false,
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| 79 |
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"normalized": false,
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| 80 |
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"rstrip": false,
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| 81 |
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"single_word": false,
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| 82 |
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"special": true
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| 83 |
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},
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| 84 |
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"100266": {
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| 85 |
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"content": "<||_unused_6_||>",
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| 86 |
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"lstrip": false,
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| 87 |
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"normalized": false,
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| 88 |
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"rstrip": false,
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| 89 |
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"single_word": false,
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| 90 |
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"special": true
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| 91 |
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},
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"100267": {
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| 93 |
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"content": "<||_unused_7_||>",
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"lstrip": false,
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| 95 |
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"normalized": false,
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| 96 |
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"rstrip": false,
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| 97 |
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"single_word": false,
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"special": true
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| 99 |
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},
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| 100 |
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"100268": {
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"content": "<||_unused_8_||>",
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"lstrip": false,
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"normalized": false,
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| 104 |
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"rstrip": false,
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| 105 |
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"single_word": false,
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| 106 |
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"special": true
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| 107 |
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},
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| 108 |
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"100269": {
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| 109 |
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"content": "<||_unused_9_||>",
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| 110 |
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"lstrip": false,
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| 111 |
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"normalized": false,
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| 112 |
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"rstrip": false,
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| 113 |
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"single_word": false,
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| 114 |
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"special": true
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| 115 |
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},
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| 116 |
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"100270": {
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"content": "<||_unused_10_||>",
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"lstrip": false,
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"normalized": false,
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| 120 |
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"rstrip": false,
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"single_word": false,
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| 122 |
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"special": true
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| 123 |
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},
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| 124 |
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"100271": {
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| 125 |
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"content": "<||_unused_11_||>",
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| 126 |
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"lstrip": false,
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| 127 |
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"normalized": false,
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| 128 |
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"rstrip": false,
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| 129 |
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"single_word": false,
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| 130 |
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"special": true
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| 131 |
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},
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| 132 |
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"100272": {
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| 133 |
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"content": "<||_unused_12_||>",
|
| 134 |
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"lstrip": false,
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| 135 |
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"normalized": false,
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| 136 |
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"rstrip": false,
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| 137 |
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"single_word": false,
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| 138 |
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"special": true
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| 139 |
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},
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| 140 |
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"100273": {
|
| 141 |
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"content": "<||_unused_13_||>",
|
| 142 |
+
"lstrip": false,
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| 143 |
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"normalized": false,
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| 144 |
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"rstrip": false,
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| 145 |
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"single_word": false,
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"special": true
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},
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"100274": {
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| 149 |
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"content": "<||_unused_14_||>",
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"lstrip": false,
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| 151 |
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"normalized": false,
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| 152 |
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"rstrip": false,
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| 153 |
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"single_word": false,
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"special": true
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| 155 |
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},
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| 156 |
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"100275": {
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"content": "<||_unused_15_||>",
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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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"100276": {
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| 165 |
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"content": "<|endofprompt|>",
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| 166 |
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"lstrip": false,
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| 167 |
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"normalized": false,
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| 168 |
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"rstrip": false,
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| 169 |
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"single_word": false,
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"special": true
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},
|
| 172 |
+
"100277": {
|
| 173 |
+
"content": "<|pad|>",
|
| 174 |
+
"lstrip": false,
|
| 175 |
+
"normalized": false,
|
| 176 |
+
"rstrip": false,
|
| 177 |
+
"single_word": false,
|
| 178 |
+
"special": true
|
| 179 |
+
},
|
| 180 |
+
"100278": {
|
| 181 |
+
"content": "<|im_start|>",
|
| 182 |
+
"lstrip": false,
|
| 183 |
+
"normalized": false,
|
| 184 |
+
"rstrip": false,
|
| 185 |
+
"single_word": false,
|
| 186 |
+
"special": true
|
| 187 |
+
},
|
| 188 |
+
"100279": {
|
| 189 |
+
"content": "<|im_end|>",
|
| 190 |
+
"lstrip": false,
|
| 191 |
+
"normalized": false,
|
| 192 |
+
"rstrip": false,
|
| 193 |
+
"single_word": false,
|
| 194 |
+
"special": true
|
| 195 |
+
}
|
| 196 |
+
},
|
| 197 |
+
"bos_token": "<|endoftext|>",
|
| 198 |
+
"clean_up_tokenization_spaces": true,
|
| 199 |
+
"eos_token": "<|endoftext|>",
|
| 200 |
+
"extra_special_tokens": {},
|
| 201 |
+
"model_max_length": 32768,
|
| 202 |
+
"pad_token": "<|pad|>",
|
| 203 |
+
"tokenizer_class": "GPT2Tokenizer",
|
| 204 |
+
"unk_token": "<|endoftext|>"
|
| 205 |
+
}
|
vocab.json
ADDED
|
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|
|