Text Generation
Transformers
English
encoder_decoder
code
natural language understanding
machine learning
research
introspection
self-reflection
conversational
Instructions to use Or4cl3-1/CSUMLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Or4cl3-1/CSUMLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Or4cl3-1/CSUMLM") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Or4cl3-1/CSUMLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Or4cl3-1/CSUMLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Or4cl3-1/CSUMLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Or4cl3-1/CSUMLM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Or4cl3-1/CSUMLM
- SGLang
How to use Or4cl3-1/CSUMLM 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 "Or4cl3-1/CSUMLM" \ --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": "Or4cl3-1/CSUMLM", "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 "Or4cl3-1/CSUMLM" \ --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": "Or4cl3-1/CSUMLM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Or4cl3-1/CSUMLM with Docker Model Runner:
docker model run hf.co/Or4cl3-1/CSUMLM
Create tokenizer_config.json
Browse files- tokenizer_config.json +18 -0
tokenizer_config.json
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{
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"tokenizer_name": "CSUMLM Tokenizer",
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"model_name": "CSUMLM",
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"description": "Tokenizer for the CognoSphere Unified Multimodal Language Model",
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"author": "Or4cl3 AI Solutions",
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"language": "Multimodal (Text, Image, Audio)",
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"vocab_size": 32000,
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"max_sequence_length": 512,
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"special_tokens": {
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"bos_token": "<BOS>",
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"eos_token": "<EOS>",
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"pad_token": "<PAD>",
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"unk_token": "<UNK>",
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"mask_token": "<MASK>"
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},
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"tokenization_method": "Byte Pair Encoding (BPE)",
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"training_data": "Custom 1500 Example Dataset"
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}
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