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
| #!/usr/bin/env python3 | |
| """Merge the name-binding LoRA into full weights on CPU -> models/cosmos_namebind_merged. | |
| Same recipe as the 2026-06-25 rebirth merge (which went to the old E: drive — gone now), | |
| targeting D: instead. CPU-only on purpose: the 4GB card can't hold fp16 1.5B + adapters, | |
| and CPU merge is a few minutes for this size. | |
| Usage: py scripts/cosmos_merge_namebind.py | |
| Env: HF_HOME should point at the cache that holds the base (C:\\Users\\corys\\hf_cache_local) | |
| """ | |
| import os | |
| import sys | |
| import time | |
| ADAPTER = os.getenv("COSMOS_NB_ADAPTER", r"D:\Cosmos\models\cosmos_namebind_lora") | |
| OUT = os.getenv("COSMOS_NB_MERGED", r"D:\Cosmos\models\cosmos_namebind_merged") | |
| BASE = os.getenv("COSMOS_FT_BASE", "Qwen/Qwen2.5-1.5B-Instruct") | |
| def main() -> int: | |
| t0 = time.time() | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from peft import PeftModel | |
| print(f"[MERGE] base={BASE}") | |
| print(f"[MERGE] adapter={ADAPTER}") | |
| model = AutoModelForCausalLM.from_pretrained(BASE, torch_dtype=torch.float16, device_map="cpu") | |
| model = PeftModel.from_pretrained(model, ADAPTER, device_map="cpu") | |
| print(f"[MERGE] merging adapters into base (cpu)... ({time.time()-t0:.0f}s)") | |
| model = model.merge_and_unload() | |
| os.makedirs(OUT, exist_ok=True) | |
| model.save_pretrained(OUT, safe_serialization=True) | |
| AutoTokenizer.from_pretrained(BASE).save_pretrained(OUT) | |
| print(f"[MERGE] DONE in {time.time()-t0:.0f}s -> {OUT}") | |
| return 0 | |
| if __name__ == "__main__": | |
| sys.exit(main()) | |