Instructions to use AesSedai/Step-3.7-Flash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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 (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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

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 serve -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 serve -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

OpenClaw new

How to use AesSedai/Step-3.7-Flash-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AesSedai/Step-3.7-Flash-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "AesSedai/Step-3.7-Flash-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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

Updates

6/2/2026: I've updated all of the quants to include the updated GGUF conversion + MTP heads (as Q8_0)

Description

This repo contains specialized MoE-quants for Step-3.7-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
Q8_0	197.43 GiB (8.51 BPW)	Q8_0	1.894568 ± 0.007218	+0.1273%	0.005301 ± 0.000052
Q5_K_M	138.83 GiB (5.98 BPW)	Q8_0 / Q5_K / Q5_K / Q6_K	1.911601 ± 0.007329	+1.0275%	0.017023 ± 0.000119
Q4_K_M	116.22 GiB (5.01 BPW)	Q8_0 / Q4_K / Q4_K / Q5_K	1.957959 ± 0.007610	+3.4775%	0.047917 ± 0.000315
IQ4_XS	91.31 GiB (3.93 BPW)	Q8_0 / IQ3_S / IQ3_S / IQ4_XS	2.187038 ± 0.009041	+15.5843%	0.159543 ± 0.000943
IQ3_S	70.89 GiB (3.05 BPW)	Q6_K / IQ2_S / IQ2_S / IQ3_S	2.915835 ± 0.013847	+54.1009%	0.459317 ± 0.002233
IQ2_S	64.43 GiB (2.78 BPW)	Q6_K / IQ2_XS / IQ2_XS / IQ3_XXS	3.443042 ± 0.017577	+81.9637%	0.623856 ± 0.002810