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: 2,143 Bytes
1c0d385 | 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 61 62 63 64 65 | """Expand v4 mix into v5: rephrase forensic/SOP users (same verified answers)
to multiply exposure to domain vocabulary, keep raw replay for fluency.
Output: data/sft_mix_v5.jsonl
"""
import json, random
from collections import Counter
from pathlib import Path
rng = random.Random(20260802)
OUT = Path("data/sft_mix_v5.jsonl")
def load(p): return [json.loads(l) for l in open(p, encoding="utf-8") if l.strip()]
PREFIXES = [
"Verify:", "Check whether:", "Assess the claim:", "Is this true?",
"Analyze:", "Examine:", "Look into:", "Evaluate the evidence for:",
"What is wrong with this claim?", "Check the facts behind:",
"Determine whether this holds:", "Scrutinize:",
]
SUFFIXES = [
" Consider the available evidence.",
" Check the sources and dates.",
" Assess the strength of the evidence.",
" Note any gaps.",
" Compare the claims involved.",
]
def rephrase(user, assistant):
variants = []
for _ in range(2):
u2 = user.strip()
prefix = rng.choice(PREFIXES)
suffix = rng.choice(SUFFIXES)
u2 = f"{prefix} {u2}{suffix}"
variants.append({"persona": "analyst", "user": u2, "assistant": assistant})
return variants
def main():
v4 = load("data/sft_mix_v4.jsonl")
out = []
n_reph = 0
for ex in v4:
out.append(ex)
u = ex.get("user", "")
if "raw" not in ex and len(u) > 80 and ex.get("persona") in ("analyst", "skeptic"):
for v in rephrase(u, ex["assistant"]):
out.append(v)
n_reph += 1
# dedupe by (persona, user)
seen, final = set(), []
for ex in out:
k = ("raw", ex.get("raw", "")[:180]) if "raw" in ex else (ex.get("persona", "?"), ex.get("user", "")[:180])
if k in seen:
continue
seen.add(k); final.append(ex)
rng.shuffle(final)
with open(OUT, "w", encoding="utf-8") as f:
for ex in final:
f.write(json.dumps(ex, ensure_ascii=False) + "\n")
print("total", len(final), "rephrased_added", n_reph, dict(Counter(e.get("persona", "?") for e in final)))
if __name__ == "__main__":
main()
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