How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf terribleplan/stepfun-ai_Step-3.5-Flash:
# Run inference directly in the terminal:
llama-cli -hf terribleplan/stepfun-ai_Step-3.5-Flash:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf terribleplan/stepfun-ai_Step-3.5-Flash:
# Run inference directly in the terminal:
llama-cli -hf terribleplan/stepfun-ai_Step-3.5-Flash:
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 terribleplan/stepfun-ai_Step-3.5-Flash:
# Run inference directly in the terminal:
./llama-cli -hf terribleplan/stepfun-ai_Step-3.5-Flash:
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 terribleplan/stepfun-ai_Step-3.5-Flash:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf terribleplan/stepfun-ai_Step-3.5-Flash:
Use Docker
docker model run hf.co/terribleplan/stepfun-ai_Step-3.5-Flash:
Quick Links

GGUF quants of stepfun-ai/Step-3.5-Flash

Quantization was performed without imatrix for the purposes comparison and experimentation. Perplixity may be worse than expected due to this naive approach.

Sample outputs and comparative evaluation coming eventually.

Name Version
stepfun-ai/Step-3.5-Flash a9197e1b758e
convert_hf_to_gguf.py, llama-quantize and llama-gguf-split b7964

See the original model card here.

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step35
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