Instructions to use AesSedai/Step-3.5-Flash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use AesSedai/Step-3.5-Flash-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="AesSedai/Step-3.5-Flash-GGUF", filename="IQ3_XXS/Step-3.5-Flash-IQ3_XXS-00001-of-00003.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use AesSedai/Step-3.5-Flash-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf AesSedai/Step-3.5-Flash-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf AesSedai/Step-3.5-Flash-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf AesSedai/Step-3.5-Flash-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf AesSedai/Step-3.5-Flash-GGUF:Q4_K_M
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 AesSedai/Step-3.5-Flash-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf AesSedai/Step-3.5-Flash-GGUF:Q4_K_M
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 AesSedai/Step-3.5-Flash-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf AesSedai/Step-3.5-Flash-GGUF:Q4_K_M
Use Docker
docker model run hf.co/AesSedai/Step-3.5-Flash-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use AesSedai/Step-3.5-Flash-GGUF with Ollama:
ollama run hf.co/AesSedai/Step-3.5-Flash-GGUF:Q4_K_M
- Unsloth Studio new
How to use AesSedai/Step-3.5-Flash-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for AesSedai/Step-3.5-Flash-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for AesSedai/Step-3.5-Flash-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for AesSedai/Step-3.5-Flash-GGUF to start chatting
- Pi new
How to use AesSedai/Step-3.5-Flash-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf AesSedai/Step-3.5-Flash-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "AesSedai/Step-3.5-Flash-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use AesSedai/Step-3.5-Flash-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf AesSedai/Step-3.5-Flash-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default AesSedai/Step-3.5-Flash-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use AesSedai/Step-3.5-Flash-GGUF with Docker Model Runner:
docker model run hf.co/AesSedai/Step-3.5-Flash-GGUF:Q4_K_M
- Lemonade
How to use AesSedai/Step-3.5-Flash-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AesSedai/Step-3.5-Flash-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Step-3.5-Flash-GGUF-Q4_K_M
List all available models
lemonade list
This repo contains specialized MoE-quants for Step-3.5-Flash. The idea being that given the huge size of the FFN tensors compared to the rest of the tensors in the model, it should be possible to achieve a better quality while keeping the overall size of the entire model smaller compared to a similar naive quantization. To that end, the quantization type default is kept in high quality and the FFN UP + FFN GATE tensors are quanted down along with the FFN DOWN tensors.
| Quant | Size | Mixture | PPL | 1-(Mean PPL(Q)/PPL(base)) | KLD |
|---|---|---|---|---|---|
| Q4_K_M | 113.82 GiB (4.96 BPW) | Q8_0 / Q4_K / Q4_K / Q5_K | 4.718049 ± 0.030373 | +0.3762% | 0.015464 ± 0.000133 |
| IQ4_XS | 88.90 GiB (3.88 BPW) | Q8_0 / IQ3_S / IQ3_S / IQ4_XS | 4.822499 ± 0.031236 | +2.5984% | 0.042753 ± 0.000301 |
| IQ3_XXS | 73.10 GiB (3.19 BPW) | Q6_K / IQ3_XXS / IQ3_XXS / IQ3_XXS | 4.882908 ± 0.031560 | +3.8836% | 0.078681 ± 0.000506 |
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Base model
stepfun-ai/Step-3.5-Flash
