Instructions to use cijov/cijov-lang-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cijov/cijov-lang-tokenizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cijov/cijov-lang-tokenizer") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cijov/cijov-lang-tokenizer", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use cijov/cijov-lang-tokenizer with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cijov/cijov-lang-tokenizer" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cijov/cijov-lang-tokenizer", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cijov/cijov-lang-tokenizer
- SGLang
How to use cijov/cijov-lang-tokenizer 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 "cijov/cijov-lang-tokenizer" \ --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": "cijov/cijov-lang-tokenizer", "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 "cijov/cijov-lang-tokenizer" \ --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": "cijov/cijov-lang-tokenizer", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cijov/cijov-lang-tokenizer with Docker Model Runner:
docker model run hf.co/cijov/cijov-lang-tokenizer
| { | |
| "model_max_length": 40960, | |
| "padding_side": "left", | |
| "bos_token": null, | |
| "eos_token": "<|im_end|>", | |
| "pad_token": "<|endoftext|>", | |
| "unk_token": null, | |
| "clean_up_tokenization_spaces": false, | |
| "add_bos_token": false, | |
| "add_eos_token": false, | |
| "add_prefix_space": false, | |
| "chat_template": "{%- for message in messages -%}\n{%- if message['role'] == 'system' -%}\n<|im_start|>system\n{{ message['content'] }}<|im_end|>\n{% elif message['role'] == 'user' -%}\n<|im_start|>user\n{{ message['content'] }}<|im_end|>\n{% elif message['role'] == 'assistant' -%}\n<|im_start|>assistant\n{{ message['content'] }}<|im_end|>\n{% endif -%}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n<|im_start|>assistant\n{%- endif -%}", | |
| "added_tokens_decoder": { | |
| "151643": { | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": false, | |
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| "single_word": false, | |
| "special": true | |
| }, | |
| "151644": { | |
| "content": "<|im_start|>", | |
| "lstrip": false, | |
| "normalized": false, | |
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| "special": true | |
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| "special": true | |
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| "special": true | |
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| "content": "<|video_pad|>", | |
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| } |