Text Generation
Transformers
Safetensors
phi3
trl
sft
conversational
custom_code
text-generation-inference
Instructions to use gguichard/phi-4-mini-full_model_dwh_diff_extraction_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gguichard/phi-4-mini-full_model_dwh_diff_extraction_data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="gguichard/phi-4-mini-full_model_dwh_diff_extraction_data", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("gguichard/phi-4-mini-full_model_dwh_diff_extraction_data", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("gguichard/phi-4-mini-full_model_dwh_diff_extraction_data", trust_remote_code=True) 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 gguichard/phi-4-mini-full_model_dwh_diff_extraction_data with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "gguichard/phi-4-mini-full_model_dwh_diff_extraction_data" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gguichard/phi-4-mini-full_model_dwh_diff_extraction_data", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/gguichard/phi-4-mini-full_model_dwh_diff_extraction_data
- SGLang
How to use gguichard/phi-4-mini-full_model_dwh_diff_extraction_data 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 "gguichard/phi-4-mini-full_model_dwh_diff_extraction_data" \ --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": "gguichard/phi-4-mini-full_model_dwh_diff_extraction_data", "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 "gguichard/phi-4-mini-full_model_dwh_diff_extraction_data" \ --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": "gguichard/phi-4-mini-full_model_dwh_diff_extraction_data", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use gguichard/phi-4-mini-full_model_dwh_diff_extraction_data with Docker Model Runner:
docker model run hf.co/gguichard/phi-4-mini-full_model_dwh_diff_extraction_data
Upload tokenizer
Browse files- .gitattributes +1 -0
- added_tokens.json +12 -0
- chat_template.jinja +1 -0
- merges.txt +0 -0
- special_tokens_map.json +30 -0
- tokenizer.json +3 -0
- tokenizer_config.json +111 -0
- vocab.json +0 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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added_tokens.json
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{
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"<|/tool_call|>": 200026,
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"<|/tool|>": 200024,
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"<|assistant|>": 200019,
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"<|end|>": 200020,
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"<|system|>": 200022,
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"<|tag|>": 200028,
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"<|tool_call|>": 200025,
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"<|tool_response|>": 200027,
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"<|tool|>": 200023,
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"<|user|>": 200021
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}
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chat_template.jinja
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{% for message in messages %}{% if message['role'] == 'system' and 'tools' in message and message['tools'] is not none %}{{ '<|' + message['role'] + '|>' + message['content'] + '<|tool|>' + message['tools'] + '<|/tool|>' + '<|end|>' }}{% else %}{{ '<|' + message['role'] + '|>' + message['content'] + '<|end|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>' }}{% else %}{{ eos_token }}{% endif %}
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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": "<|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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"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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version https://git-lfs.github.com/spec/v1
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oid sha256:382cc235b56c725945e149cc25f191da667c836655efd0857b004320e90e91ea
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size 15524095
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tokenizer_config.json
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{
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"add_bos_token": false,
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"add_eos_token": false,
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"add_prefix_space": false,
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"added_tokens_decoder": {
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"199999": {
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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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"200018": {
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"content": "<|endofprompt|>",
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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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"200019": {
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"content": "<|assistant|>",
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"lstrip": false,
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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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"200020": {
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"content": "<|end|>",
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"lstrip": false,
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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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"200021": {
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"content": "<|user|>",
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"lstrip": false,
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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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"200022": {
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"content": "<|system|>",
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"lstrip": false,
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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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"200023": {
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"content": "<|tool|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": true,
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"single_word": false,
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"special": false
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},
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"200024": {
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"content": "<|/tool|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": true,
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"single_word": false,
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"special": false
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},
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"200025": {
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"content": "<|tool_call|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": true,
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"single_word": false,
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"special": false
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},
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"200026": {
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"content": "<|/tool_call|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": true,
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"single_word": false,
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"special": false
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},
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"200027": {
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"content": "<|tool_response|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": true,
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"single_word": false,
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"special": false
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},
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"200028": {
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"content": "<|tag|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": true,
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"single_word": false,
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| 100 |
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"special": true
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| 101 |
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}
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},
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| 103 |
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"bos_token": "<|endoftext|>",
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| 104 |
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"clean_up_tokenization_spaces": false,
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| 105 |
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"eos_token": "<|endoftext|>",
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| 106 |
+
"extra_special_tokens": {},
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| 107 |
+
"model_max_length": 131072,
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| 108 |
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"pad_token": "<|endoftext|>",
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| 109 |
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"tokenizer_class": "GPT2Tokenizer",
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| 110 |
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"unk_token": "<|endoftext|>"
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| 111 |
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}
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vocab.json
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