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
File size: 2,594 Bytes
a6bb96b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | # COSMOS Performance Profile
## Multi-Turn Chat Performance (5 turns)
| Turn | Prompt | Latency (s) | Peak RSS (MB) | Exit Code |
|------|--------|-------------|--------------|-----------|
| 1 | Hello Cosmos! How are you feeling right now? | 7.716 | 24.30 | 0 |
| 2 | Explain briefly CST phase attention | 5.057 | 24.41 | 0 |
| 3 | Summarize Hebbian plasticity learning rule | 5.858 | 24.56 | 0 |
| 4 | Write pseudocode for persistent memory update | 7.984 | 24.46 | 0 |
| 5 | Safety note about online plasticity | 2.916 | 24.42 | 0 |
**Observations:**
- Average latency: 5.9 s per response
- Memory usage stable: ~24–25 MB (CLI process peak)
- All requests completed successfully
## 20-Turn Continuous Chat (Context Persistence Test)
Testing Hebbian plasticity and context retention over extended conversation.
| Turn | Latency (s) | Output Chars |
|------|-------------|--------------|
| 1 | 2.083 | 19 | | 2 | 2.621 | 99 | | 3 | 2.617 | 90 | | 4 | 6.302 | 402 | | 5 | 7.565 | 595 | | 6 | 4.134 | 222 | | 7 | 3.106 | 129 | | 8 | 11.204 | 982 | | 9 | 5.459 | 358 | | 10 | 2.83 | 123 | | 11 | 5.496 | 311 | | 12 | 3.101 | 144 | | 13 | 17.624 | 1626 | | 14 | 10.17 | 793 | | 15 | 9.594 | 735 | | 16 | 6.993 | 505 | | 17 | 5.163 | 327 | | 18 | 5.46 | 378 | | 19 | 4.685 | 347 | | 20 | 4.405 | 278 |
**Observations:**
- Average latency across 20 turns: 6.031 s
- Total conversation time: 120.612 s
- No latency degradation observed over extended turns (indicates stable context handling)
## Concurrent Request Performance (3 parallel requests)
Testing throughput and concurrency on local machine.
| Request | Output Chars |
|---------|--------------|
| 1 | 62 | | 2 | 1385 | | 3 | 358 |
**Total time for 3 parallel requests:** 23.0749368 s
**Observations:**
- All 3 requests completed in parallel without errors
- Total time ~23.07s (faster than serial execution)
## System & Model Info
- Model: COSMOS (54D) Q4-quantized GGUF
- Local runtime: Ollama
- Machine: Windows (reported peak memory ~24–25 MB for ollama CLI process)
- Model size: 3.1 GB
- Quantization: Q4
## Recommendations
- Model is suitable for interactive local inference on CPU
- Memory footprint is minimal for the CLI process; GPU/host memory needs detailed profiling
- No performance degradation over 20+ turns suggests stable context and plasticity handling
- Concurrent request support confirmed on local machine
## Next Steps
- Test on Atomic AI platform (iOS/remote runtime)
- Profile GPU memory usage if available
- Test with longer context windows (8K+)
- Implement token-per-second (TPS) benchmarking
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