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
| """Build evidence-in-context judgment dataset: claim + evidence -> verdict. | |
| Dedupe across all SFT corpora; keeps rows where the user text contains both a | |
| claim and a separate evidence marker, with a parseable Verdict label.""" | |
| import json, re | |
| from collections import Counter | |
| from pathlib import Path | |
| EVID = re.compile(r"records? show|permit|account [ab]|accounts? [ab]:|according to|filing|inspection|report[sed]? (by|that|the)|data show|documents|registered|confirmed|assessor|timeline|witness|source|wire|blog|notes? that|second investigation|reassessment|raised? [0-9]|rose [0-9]|increas[ed]? [0-9]", re.I) | |
| VALS = ["true statement", "false statement", "supports", "refutes", "not_enough_info"] | |
| def parse_v(text): | |
| m = re.search(r"Verdict\s*:\s*([^.]+)\.", text, re.I) | |
| if not m: return None | |
| lab = m.group(1).strip().lower() | |
| for cand in VALS: | |
| if lab.startswith(cand): return cand | |
| if lab.startswith("true"): return "true statement" | |
| if lab.startswith("false"): return "false statement" | |
| if lab.startswith("not"): return "not_enough_info" | |
| if lab.startswith("support"): return "supports" | |
| if lab.startswith("refut"): return "refutes" | |
| return None | |
| def load(p): | |
| return [json.loads(l) for l in open(p, encoding="utf-8") if l.strip()] | |
| files = ["sft_forensic.jsonl","sft_sop_mix.jsonl","sft_sop.jsonl","sft_distill_mix.jsonl", | |
| "distill_method.jsonl","distill_analyst_a.jsonl","distill_analyst_b.jsonl", | |
| "sft_mix_v2.jsonl","sft_mix_v3.jsonl"] | |
| seen, out = set(), [] | |
| for f in files: | |
| for r in load(f"data/{f}"): | |
| u, a = r.get("user", ""), r.get("assistant", "") | |
| v = parse_v(a) | |
| if not v or not EVID.search(u): | |
| continue | |
| key = u[:200] | |
| if key in seen: | |
| continue | |
| seen.add(key) | |
| out.append({"persona": r.get("persona", "analyst"), "user": u, "verdict": v}) | |
| print("total", len(out), dict(Counter(o["verdict"] for o in out)), flush=True) | |
| with open("data/evidence_judge.jsonl", "w", encoding="utf-8") as f: | |
| for o in out: | |
| f.write(json.dumps(o, ensure_ascii=False) + "\n") | |