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: 2,011 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 | encoded 16,000,033 tokens in 42s (384,069 tok/s)
encoded 32,000,050 tokens in 83s (384,311 tok/s)
encoded 48,000,083 tokens in 125s (384,031 tok/s)
encoded 64,000,091 tokens in 167s (384,029 tok/s)
encoded 80,000,100 tokens in 208s (384,042 tok/s)
encoded 96,000,123 tokens in 250s (384,184 tok/s)
encoded 112,000,166 tokens in 291s (384,237 tok/s)
encoded 128,000,194 tokens in 333s (384,088 tok/s)
encoded 144,000,210 tokens in 374s (384,922 tok/s)
encoded 160,000,216 tokens in 409s (391,128 tok/s)
encoded 176,000,235 tokens in 446s (394,292 tok/s)
encoded 192,000,265 tokens in 488s (393,261 tok/s)
encoded 208,000,283 tokens in 527s (394,666 tok/s)
encoded 224,000,384 tokens in 562s (398,464 tok/s)
encoded 240,000,427 tokens in 594s (403,956 tok/s)
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encoded 272,000,457 tokens in 671s (405,435 tok/s)
encoded 288,000,460 tokens in 707s (407,516 tok/s)
encoded 304,000,488 tokens in 745s (408,291 tok/s)
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encoded 336,000,509 tokens in 823s (408,112 tok/s)
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encoded 368,000,604 tokens in 918s (400,707 tok/s)
encoded 384,000,626 tokens in 953s (402,986 tok/s)
encoded 400,000,639 tokens in 987s (405,198 tok/s)
encoded 416,000,682 tokens in 1021s (407,291 tok/s)
encoded 432,000,712 tokens in 1056s (409,252 tok/s)
encoded 448,000,750 tokens in 1090s (410,937 tok/s)
encoded 464,000,783 tokens in 1124s (412,633 tok/s)
encoded 480,000,798 tokens in 1159s (414,255 tok/s)
encoded 496,000,823 tokens in 1194s (415,460 tok/s)
encoded 512,000,832 tokens in 1228s (416,917 tok/s)
encoded 528,000,852 tokens in 1262s (418,327 tok/s)
Traceback (most recent call last):
File "/root/Documents/Codex/2026-07-31/so-i-ve-got-a-task/data/encode_full.py", line 49, in <module>
main()
File "/root/Documents/Codex/2026-07-31/so-i-ve-got-a-task/data/encode_full.py", line 45, in main
np.asarray(buf, dtype=np.uint16).tofile(g)
ValueError: flush of closed file
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