Instructions to use AesSedai/Step-3.7-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-3.7-Flash-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="AesSedai/Step-3.7-Flash-GGUF", filename="IQ2_S/Step-3.7-Flash-IQ2_S-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 Settings
- llama.cpp
How to use AesSedai/Step-3.7-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-3.7-Flash-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf AesSedai/Step-3.7-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-3.7-Flash-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf AesSedai/Step-3.7-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-3.7-Flash-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf AesSedai/Step-3.7-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-3.7-Flash-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf AesSedai/Step-3.7-Flash-GGUF:Q4_K_M
Use Docker
docker model run hf.co/AesSedai/Step-3.7-Flash-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use AesSedai/Step-3.7-Flash-GGUF with Ollama:
ollama run hf.co/AesSedai/Step-3.7-Flash-GGUF:Q4_K_M
- Unsloth Studio
How to use AesSedai/Step-3.7-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-3.7-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-3.7-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-3.7-Flash-GGUF to start chatting
- Pi
How to use AesSedai/Step-3.7-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-3.7-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-3.7-Flash-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use AesSedai/Step-3.7-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-3.7-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-3.7-Flash-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use AesSedai/Step-3.7-Flash-GGUF with Docker Model Runner:
docker model run hf.co/AesSedai/Step-3.7-Flash-GGUF:Q4_K_M
- Lemonade
How to use AesSedai/Step-3.7-Flash-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AesSedai/Step-3.7-Flash-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Step-3.7-Flash-GGUF-Q4_K_M
List all available models
lemonade list
New upload taking more VRAM with MTP disabled?
Hey AesSedai, love your models
Maybe you can help me out. I was running this model, IQ3_S with 167k context yesterday, today new GGUFs were uploaded, and now only 110k fits, even a bit more context and I'm OOM
Do MTP layers take extra VRAM even if MTP is not enabled? I don't have any extra VRAM/RAM to enable MTP anyway so I'd prefer running the model without it, but it's unfortunate that the new GGUFs cause me to have less context. Or is it a bug in llama.cpp which allocates extra memory even if MTP is not enabled?
Thanks!
Hi, I don't know if it requires more VRAM with MTP disabled, I would think not but it sounds like it might be worth an issue on the llama.cpp github if you can provide some more detail maybe?
I'd investigate a bit more, but I deleted the previous GGUFs of your model. Do you know if I can get the IQ3_S from before the latest upload? Am I correct that the latest reupload was adding MTP?
If I had the previous GGUF I'd at least be able to investigate why llama.cpp eats more VRAM with the newer model with mtp disabled.
I've been squashing the repository after uploads to save space, so the previous GGUFs are gone :(
Ах-ха-ха-ха! =D
I'm too delete file, download new and… =D
No vision today, kekeke.