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
| """Teacher-distillation dataset builder. | |
| Merges hand-written teacher knowledge files (data/distill_*.jsonl) with the | |
| existing forensic SFT set into data/sft_distill.jsonl, deduplicated by user | |
| text. Each entry: {"persona": "analyst"|"skeptic", "user": ..., "assistant": ...} | |
| Usage: | |
| .venv/bin/python data/distill.py | |
| """ | |
| import hashlib | |
| import json | |
| import random | |
| from pathlib import Path | |
| HERE = Path(__file__).parent | |
| OUT = HERE / "sft_distill.jsonl" | |
| def main(): | |
| random.seed(13) | |
| seen, examples = set(), [] | |
| sources = sorted(HERE.glob("distill_*.jsonl")) + [HERE / "seed_forensic.jsonl"] | |
| for src in sources: | |
| if not src.exists(): | |
| continue | |
| for line in src.read_text(encoding="utf-8").splitlines(): | |
| line = line.strip() | |
| if not line: | |
| continue | |
| ex = json.loads(line) | |
| h = hashlib.sha256(ex["user"].encode()).hexdigest() | |
| if h in seen: | |
| continue | |
| seen.add(h) | |
| examples.append(ex) | |
| random.shuffle(examples) | |
| with open(OUT, "w", encoding="utf-8") as f: | |
| for ex in examples: | |
| f.write(json.dumps(ex) + "\n") | |
| n_p = {} | |
| for ex in examples: | |
| n_p[ex["persona"]] = n_p.get(ex["persona"], 0) + 1 | |
| print(f"wrote {len(examples)} examples -> {OUT} personas={n_p}") | |
| if __name__ == "__main__": | |
| main() | |