Instructions to use FerrellSyntheticIntelligence/fsi-anomaly 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 FerrellSyntheticIntelligence/fsi-anomaly 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 FerrellSyntheticIntelligence/fsi-anomaly # Run inference directly in the terminal: llama cli -hf FerrellSyntheticIntelligence/fsi-anomaly
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf FerrellSyntheticIntelligence/fsi-anomaly # Run inference directly in the terminal: llama cli -hf FerrellSyntheticIntelligence/fsi-anomaly
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 FerrellSyntheticIntelligence/fsi-anomaly # Run inference directly in the terminal: ./llama-cli -hf FerrellSyntheticIntelligence/fsi-anomaly
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 FerrellSyntheticIntelligence/fsi-anomaly # Run inference directly in the terminal: ./build/bin/llama-cli -hf FerrellSyntheticIntelligence/fsi-anomaly
Use Docker
docker model run hf.co/FerrellSyntheticIntelligence/fsi-anomaly
- LM Studio
- Jan
- Ollama
How to use FerrellSyntheticIntelligence/fsi-anomaly with Ollama:
ollama run hf.co/FerrellSyntheticIntelligence/fsi-anomaly
- Unsloth Desktop
- Docker Model Runner
How to use FerrellSyntheticIntelligence/fsi-anomaly with Docker Model Runner:
docker model run hf.co/FerrellSyntheticIntelligence/fsi-anomaly
- Lemonade
How to use FerrellSyntheticIntelligence/fsi-anomaly with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull FerrellSyntheticIntelligence/fsi-anomaly
Run and chat with the model
lemonade run user.fsi-anomaly-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 1,587 Bytes
d83b47a | 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 | """Publish hf_repo to Hugging Face.
1. Login: huggingface-cli login (or set HF_TOKEN)
2. Run: .venv/bin/python hf_upload.py --repo YOUR_ORG/tiny-liquid-analyst
Creates the repo if missing, uploads every file in hf_repo/, and prints the
model page URL. Requires huggingface_hub (installed).
"""
import argparse
import sys
from pathlib import Path
from huggingface_hub import HfApi
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--repo", default="YOUR_ORG/tiny-liquid-analyst",
help="HF repo id, e.g. yourname/tiny-liquid-analyst")
ap.add_argument("--dir", default="hf_repo")
ap.add_argument("--private", action="store_true", help="create as private repo")
args = ap.parse_args()
if args.repo.startswith("YOUR_ORG"):
sys.exit("set --repo to your HF repo id (e.g. yourname/tiny-liquid-analyst)")
api = HfApi()
try:
api.repo_info(args.repo)
print(f"repo exists: {args.repo}")
except Exception:
api.create_repo(args.repo, private=args.private, repo_type="model")
print(f"created repo: {args.repo}")
files = sorted(Path(args.dir).rglob("*"))
files = [f for f in files if f.is_file() and f.name not in (".DS_Store",)]
paths = [str(f) for f in files]
api.upload_folder(folder_path=args.dir, repo_id=args.repo, repo_type="model")
print(f"uploaded {len(paths)} files -> https://huggingface.co/{args.repo}")
print("next: edit the model card link in hf_repo/README.md and re-upload, or set it via the web UI.")
if __name__ == "__main__":
main()
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