Text Generation
Transformers
TensorBoard
Safetensors
English
conversational
quantum-math
PEFT
Safetensors
AutoTrain
Eval Results (legacy)
Instructions to use shafire/QuantumAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shafire/QuantumAI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shafire/QuantumAI") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("shafire/QuantumAI", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use shafire/QuantumAI with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "shafire/QuantumAI" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shafire/QuantumAI", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/shafire/QuantumAI
- SGLang
How to use shafire/QuantumAI 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 "shafire/QuantumAI" \ --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": "shafire/QuantumAI", "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 "shafire/QuantumAI" \ --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": "shafire/QuantumAI", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use shafire/QuantumAI with Docker Model Runner:
docker model run hf.co/shafire/QuantumAI
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# **QuantumAI: Zero LLM Quantum AI Model**
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**Zero Quantum AI** is an advanced text-generation model built using interdimensional mathematics, quantum math, and the **Mathematical Probability of Goodness**. Developed by **TalkToAi.org** and **ResearchForum.Online**, this model leverages cutting-edge AI frameworks to redefine conversational AI, ensuring deep, ethical decision-making capabilities. The model is fine-tuned on **Meta-Llama-3.1-8B-Instruct** and trained via **AutoTrain** to optimize conversational tasks, dialogue generation, and inference.
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---
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language: en
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tags:
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- text-generation
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- transformers
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- conversational
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- quantum-math
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- PEFT
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- Safetensors
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- AutoTrain
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license: other
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datasets: conversational-dataset
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model-index:
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- name: Zero LLM Quantum AI
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results:
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- task:
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type: text-generation
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dataset:
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name: conversational-dataset
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type: text
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metrics:
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- name: Training Loss
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type: loss
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value: 1.74
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---
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# **QuantumAI: Zero LLM Quantum AI Model**
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**Zero Quantum AI** is an advanced text-generation model built using interdimensional mathematics, quantum math, and the **Mathematical Probability of Goodness**. Developed by **TalkToAi.org** and **ResearchForum.Online**, this model leverages cutting-edge AI frameworks to redefine conversational AI, ensuring deep, ethical decision-making capabilities. The model is fine-tuned on **Meta-Llama-3.1-8B-Instruct** and trained via **AutoTrain** to optimize conversational tasks, dialogue generation, and inference.
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