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

q3 for 24vram + 64ram systems please?

by amane2 - opened Feb 7

Discussion

amane2

Feb 7

q3 for 24vram + 64ram systems please?

ubergarm

Owner Feb 7

yeah now that ik support is merged into main: https://github.com/ikawrakow/ik_llama.cpp/pull/1240

i'll cook a few smaller quants including that range using imatrix

ubergarm

Owner Feb 7

•

edited Feb 7

@amane2

I'm awake and cooking again, imatrix is uploaded.

I'm fishing for best perplexity in your target size, this one might be a little too big if you want longer context, but would likely fit 64k at q8_0 barely maybe?

smol-IQ3_KS 77.156 GiB (3.365 BPW)
IQ2_KL 71.527 GiB (3.120 BPW)

Otherwise I'm checking a slightly smaller IQ2_KL ... I'm leaving the attn/shexp/first 3 dense all Q8_0 which will give best quality at a slight cost to size and TG speed (due to larger active weights going through memory bandwidth)...

I'll holler after I graph it and get a better feel for the curve!

UPDATE Got some test quants benchmarked... If i can fit 64k context with the smol-IQ3_KS in under 64GiB+24GiB i'll likely release that one. It's looking pretty tight, and may require you to run only 32k context or knock down to like q6_0 or q4_0 with khardamon transform or something to fit it, especially if you're not running headless or need a browser too...

llm_load_tensors:        CPU buffer size = 79007.73 MiB
llama_new_context_with_model: KV self size  = 6120.00 MiB, K (q8_0): 3060.00 MiB, V (q8_0): 3060.00 MiB
llama_new_context_with_model:        CPU  output buffer size =     0.49 MiB
llama_new_context_with_model:        CPU compute buffer size =  2078.00 MiB

ubergarm

Owner Feb 7

•

edited Feb 7

I may have found a winner... interestingly knocking all attn/shexp/first 3 dense from full q8_0 down to iq6_k was barely noticible at all (in perplexity)... and saves a little over a GiB which might be "enough" hah...

Okay, uploading the smol-IQ3_KS 75.934 GiB (3.312 BPW) now!

BingoBird

Mar 1

To go boldly where no quantizer has gone before... could someone with the finacial means send some go-juice over to Ubergarm? Seriously...

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