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", dtype="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
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---
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# CognoSphere Unified Multimodal Language Model (CSUMLM) Model Card
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language:
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- en
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datasets:
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- epinnock/software-architecture-instructions
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- epinnock/software-architecture-instructions-preference
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- freecs/ArtificialThinkerSet
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- codeparrot/apps
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- deepmind/code_contests
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- clinc/cs_convo_self
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- dstc8-schema-guided-dialog
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- empathetic-dialogues
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- reddit-self-reflection
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- dialogpt/intents-full
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license: apache-2.0
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tags:
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- code
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- natural language understanding
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- machine learning
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- research
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- introspection
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- self-reflection
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- conversational
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pipeline_tag: text-generation
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library_name: transformers
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metrics:
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- accuracy
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- bertscore
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- code_eval
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contact:
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author: Dustin Groves
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organization: Or4cl3 AI Solutions
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email: dustin.groves@or4cl3.ai
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---
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# CognoSphere Unified Multimodal Language Model (CSUMLM) Model Card
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