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

Libraries

How to use ubergarm/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="ubergarm/Step-3.5-Flash-GGUF",
	filename="IQ4_XS/Step-3.5-Flash-IQ4_XS-00001-of-00004.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ubergarm/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 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-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

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-server -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-server -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

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

IQ2_KS model merging failed

by lan0004 - opened Feb 7

Discussion

lan0004

Feb 7

I encountered an error after merging the downloaded IQ2_KS model. The information is as follows:

gguf_merge: c:\Users\nova\Step-3.5-Flash-GGUF\smol-IQ2_KS\Step-3.5-Flash-smol-IQ2_KS-00001-of-00003.gguf -> d:\Step-3.5-Flash-smol-IQ2_KS.gguf
gguf_merge: reading metadata c:\Users\nova\Step-3.5-Flash-GGUF\smol-IQ2_KS\Step-3.5-Flash-smol-IQ2_KS-00001-of-00003.gguf done
gguf_merge: reading metadata c:\Users\nova\Step-3.5-Flash-GGUF\smol-IQ2_KS\Step-3.5-Flash-smol-IQ2_KS-00002-of-00003.gguf ...gguf_init_from_file_impl: tensor 'token_embd.weight' has invalid ggml type 139 (NONE)
gguf_init_from_file_impl: failed to read tensor info

gguf_merge: failed to load input GGUF from c:\Users\nova\Step-3.5-Flash-GGUF\smol-IQ2_KS\Step-3.5-Flash-smol-IQ2_KS-00001-of-00003.gguf

deleted

Feb 7

•

edited Feb 7

Same on IQ3_KS:

gguf_init_from_file_impl: tensor 'token_embd.weight' has invalid ggml type 139 (NONE)

ubergarm

Owner Feb 7

•

edited Feb 7

@lan0004 @JoeSmith245

You need to use ikawrakow/ik_llama.cpp for most of these quants including the IQ2_KS. Check the model card here for instructions getting started with it: https://huggingface.co/ubergarm/Step-3.5-Flash-GGUF#quick-start

You can see the issue grepping the code like so, here ik_llama.cpp has the needful:

$ cd ik_llama.cpp/gguf-py/
$ git rev-parse --short HEAD
e22b2d12
$ grep -r 139
gguf/constants.py:    IQ4_K     = 139
gguf/constants.py:    MOSTLY_IQ3_K         = 139 #except 1d tensors

If you try that on mainline llama.cpp, you'll see it is missing the new ik_llama.cpp SOTA quants:

$ cd llama.cpp/gguf-py/
$ grep -r 139
# there is nothing

Hope that helps! Let me know if you need more help getting ik_llama.cpp to run, you can check out windows builds here: https://github.com/Thireus/ik_llama.cpp/releases

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