Instructions to use AesSedai/Step-3.5-Flash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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="IQ2_S/Step-3.5-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-3.5-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

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

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

Atomic Chat new
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

Step-3.5-Flash-GGUF

File size: 1,971 Bytes

---
base_model:
- stepfun-ai/Step-3.5-Flash
---

## Updates
- 6/3/2026: I've updated all of the quants to include the updated GGUF conversion + MTP heads (as Q8_0)

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                 |
| :----- | :-------------------- | :------------------------------- | :------------------ | :------------------------ | :------------------ |
| Q8_0   | 197.43 GiB (8.51 BPW) | Q8_0                             | 2.419187 ± 0.011093 | +0.1197%                  | 0.005594 ± 0.000041 |
| Q5_K_M | 138.83 GiB (5.98 BPW) | Q8_0 / Q5_K / Q5_K / Q6_K        | 2.431812 ± 0.011175 | +0.6421%                  | 0.015249 ± 0.000098 |
| Q4_K_M | 116.22 GiB (5.01 BPW) | Q8_0 / Q4_K / Q4_K / Q5_K        | 2.477162 ± 0.011502 | +2.5190%                  | 0.040282 ± 0.000237 |
| IQ4_XS | 91.31 GiB (3.93 BPW)  | Q8_0 / IQ3_S / IQ3_S / IQ4_XS    | 2.685042 ± 0.012863 | +11.1222%                 | 0.127994 ± 0.000696 |
| IQ3_S  | 70.89 GiB (3.05 BPW)  | Q6_K / IQ2_S / IQ2_S / IQ3_S     | 3.313628 ± 0.017314 | +37.1367%                 | 0.355228 ± 0.001705 |
| IQ2_S  | 64.43 GiB (2.78 BPW)  | Q6_K / IQ2_XS / IQ2_XS / IQ3_XXS | 3.772340 ± 0.020688 | +56.1208%                 | 0.488752 ± 0.002225 |

![kld_graph](kld_data/01_kld_vs_filesize.png "Chart showing Pareto KLD analysis of quants")
![ppl_graph](kld_data/02_ppl_vs_filesize.png "Chart showing Pareto PPL analysis of quants")