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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ubergarm/Step-3.5-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 ubergarm/Step-3.5-Flash-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf ubergarm/Step-3.5-Flash-GGUF:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ubergarm/Step-3.5-Flash-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf ubergarm/Step-3.5-Flash-GGUF:IQ4_XS

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 ubergarm/Step-3.5-Flash-GGUF:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/Step-3.5-Flash-GGUF:IQ4_XS

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 ubergarm/Step-3.5-Flash-GGUF:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/Step-3.5-Flash-GGUF:IQ4_XS

Use Docker

docker model run hf.co/ubergarm/Step-3.5-Flash-GGUF:IQ4_XS

LM Studio
Jan

vLLM

How to use ubergarm/Step-3.5-Flash-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/Step-3.5-Flash-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ubergarm/Step-3.5-Flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/Step-3.5-Flash-GGUF:IQ4_XS

Ollama
How to use ubergarm/Step-3.5-Flash-GGUF with Ollama:
```
ollama run hf.co/ubergarm/Step-3.5-Flash-GGUF:IQ4_XS
```

Unsloth Studio

How to use ubergarm/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 ubergarm/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 ubergarm/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 ubergarm/Step-3.5-Flash-GGUF to start chatting

How to use ubergarm/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 serve -hf ubergarm/Step-3.5-Flash-GGUF:IQ4_XS

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": "ubergarm/Step-3.5-Flash-GGUF:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ubergarm/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 serve -hf ubergarm/Step-3.5-Flash-GGUF:IQ4_XS

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 ubergarm/Step-3.5-Flash-GGUF:IQ4_XS

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ubergarm/Step-3.5-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 ubergarm/Step-3.5-Flash-GGUF:IQ4_XS

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 "ubergarm/Step-3.5-Flash-GGUF:IQ4_XS" \
  --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 ubergarm/Step-3.5-Flash-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/Step-3.5-Flash-GGUF:IQ4_XS
```

Lemonade

How to use ubergarm/Step-3.5-Flash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/Step-3.5-Flash-GGUF:IQ4_XS

Run and chat with the model

lemonade run user.Step-3.5-Flash-GGUF-IQ4_XS

List all available models

lemonade list

Quant fail?

by nimishchaudhari - opened Feb 7

Discussion

nimishchaudhari

Feb 7

Trying to run smol_IQ3

"Step-3.5-Flash":
cmd: |
${ik_llama_cpp}
-m ${models_dir}/LLMs/Step-3.5-Flash/smol-IQ3_KS/Step-3.5-Flash-smol-IQ3_KS-00001-of-00003.gguf
--chat-template-file /home/nimish/Models/LLMs/Step-3.5-Flash/smol-IQ3_KS/step-3.5.jinja
--jinja
--temp 1.0
-ngl 99
-ot exps=CPU
-c 65565

ubergarm

Owner Feb 7

•

edited Feb 7

@nimishchaudhari

That looks like some kind of looping. What exact version are you running e.g. ./build/bin/llama-server --version and give the git hash. The working ik_llama.cpp PR was merged just ~16 hours ago here: https://github.com/ikawrakow/ik_llama.cpp/pull/1240

Also, in my own testing just use the included chat template e.g. --jinja but remove --chat-template-file ... or supposedly this one is working well for tool use: https://github.com/ikawrakow/ik_llama.cpp/pull/1240

Finally, what backend are you using e.g. compiled with CUDA and what GPUs and I'm assuming Linux (though your command looks more like yaml than bash?)

One more idea: you can run with --validate-quants to make sure your download didn't introduce any NaNs etc.

nimishchaudhari

Feb 8

•

edited Feb 8

cmake -B ./build -DGGML_CUDA=ON -DGGML_BLAS=OFF
These build options I might be missing some? :3, and yeah I'm on latest main of ik_llama.cpp .

Running Cuda 3090 + 3060 12GB + 192GB DDR5 5600.

nimish@jetpack-ai:~/Programs/ik_llama.cpp$ ./build/bin/llama-server --version
version: 4189 (e22b2d12)
built with cc (GCC) 15.2.1 20251211 (Red Hat 15.2.1-5) for x86_64-redhat-linux

xldistance

Feb 8

I can run it using smol-IQ2_KS, but the long context performance degradation is too severe

ubergarm

Owner Feb 8

@nimishchaudhari

I see one more commit new from ik regarding missing rope_frequency, i don't think that is something on my quants, but still catching up.

You should be able to use -sm graph with this model pretty sure, this is recommended to try when u have multi GPUs, check out this example command here: https://huggingface.co/ubergarm/Step-3.5-Flash-GGUF/discussions/8

@xldistance

The long context speed drop generally depends on the memory bandwidth you have available, and i assume it would have a similar drop rate for different size quants as well. What is your rig/cpu/ram/GPU(s) and full command. Have you tried benchmarking with llama-sweep-bench to visualize the drop off? (i might do that today). Also if you have multi GPU def use -sm graph

ubergarm

Owner Feb 8

@xldistance

The drop off on this model doesn't look bad on full 2x GPU offload using ik_llama.cpp -sm graph feature and full f16 kv cache. Are you quantizing your cache or how are you using it?

The llama-server command for this just got added to the quick start section of the model card.

xldistance

Feb 9

•

edited Feb 9

@ubergarm
https://github.com/ikawrakow/ik_llama.cpp/issues/1254#issuecomment-3867417025

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment