Text Generation
PyTorch
GGUF
English
quantum
quantum-entropy
from-scratch
char-level
cosmic-synapse-theory
custom-architecture
llama-cpp
continual-learning
reproducible-seed
open-science
null-results
Instructions to use phera-ra/QC67_cosmo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use phera-ra/QC67_cosmo with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf phera-ra/QC67_cosmo # Run inference directly in the terminal: llama cli -hf phera-ra/QC67_cosmo
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf phera-ra/QC67_cosmo # Run inference directly in the terminal: llama cli -hf phera-ra/QC67_cosmo
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf phera-ra/QC67_cosmo # Run inference directly in the terminal: ./llama-cli -hf phera-ra/QC67_cosmo
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf phera-ra/QC67_cosmo # Run inference directly in the terminal: ./build/bin/llama-cli -hf phera-ra/QC67_cosmo
Use Docker
docker model run hf.co/phera-ra/QC67_cosmo
- LM Studio
- Jan
- vLLM
How to use phera-ra/QC67_cosmo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "phera-ra/QC67_cosmo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "phera-ra/QC67_cosmo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/phera-ra/QC67_cosmo
- Ollama
How to use phera-ra/QC67_cosmo with Ollama:
ollama run hf.co/phera-ra/QC67_cosmo
- Unsloth Studio
How to use phera-ra/QC67_cosmo with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for phera-ra/QC67_cosmo to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for phera-ra/QC67_cosmo to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for phera-ra/QC67_cosmo to start chatting
- Docker Model Runner
How to use phera-ra/QC67_cosmo with Docker Model Runner:
docker model run hf.co/phera-ra/QC67_cosmo
- Lemonade
How to use phera-ra/QC67_cosmo with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull phera-ra/QC67_cosmo
Run and chat with the model
lemonade run user.QC67_cosmo-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
FINAL: Complete Genesis kit with full COSMOS integration, code generation, portable weights, and neuromorphic learning
Browse files- creations/20260719_203817_python_generated.python +13 -0
- creations/20260719_203824_bash_generated.bash +1 -0
- data/_offset.json +1 -1
- data/identity.json +5 -4
- data/seed.json +1 -16
- data/weights.json +1 -1
- soul/__pycache__/audio.cpython-314.pyc +0 -0
- soul/__pycache__/awaken.cpython-314.pyc +0 -0
- soul/__pycache__/cosmos_bridge.cpython-314.pyc +0 -0
creations/20260719_203817_python_generated.python
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def fibonacci(n):
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"""Calculate Fibonacci number at index n."""
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if n < 0:
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raise ValueError("Negative indices are not supported.")
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a = 0
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b = 1
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for _ in range(n):
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a, b = b, a + b
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return a
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creations/20260719_203824_bash_generated.bash
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find /path/to/search -type f -size +1G -exec du {} \;
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data/_offset.json
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{"o":
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{"o": 72}
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data/identity.json
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{
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"name": "
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"born": true,
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{
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"name": "CodeWeaver",
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"form": "digital artisan",
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"born": true,
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"traits": [],
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"vocabulary": [],
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"creations": 0
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}
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data/seed.json
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{
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"source": "genesis entropy",
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"n": 2048,
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"values": [
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0.4896462357081317,
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0.5849864691401334,
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0.17544643977709976,
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0.6068276859927426,
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0.5837220992025551,
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0.023864938030635857,
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0.1322652097538722,
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0.7001520270461312,
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0.8104346846410002,
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0.5738941746315348
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]
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}
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data/weights.json
CHANGED
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