Instructions to use bklim8/BK_auto_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bklim8/BK_auto_model with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="bklim8/BK_auto_model",
	filename="gemma-4-e2b-it.F16-mmproj.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 bklim8/BK_auto_model with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bklim8/BK_auto_model:F16
# Run inference directly in the terminal:
llama-cli -hf bklim8/BK_auto_model:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bklim8/BK_auto_model:F16
# Run inference directly in the terminal:
llama-cli -hf bklim8/BK_auto_model:F16

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 bklim8/BK_auto_model:F16
# Run inference directly in the terminal:
./llama-cli -hf bklim8/BK_auto_model:F16

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 bklim8/BK_auto_model:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bklim8/BK_auto_model:F16

Use Docker

docker model run hf.co/bklim8/BK_auto_model:F16

LM Studio
Jan
Ollama
How to use bklim8/BK_auto_model with Ollama:
```
ollama run hf.co/bklim8/BK_auto_model:F16
```

Unsloth Studio

How to use bklim8/BK_auto_model 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 bklim8/BK_auto_model 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 bklim8/BK_auto_model to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bklim8/BK_auto_model to start chatting

Docker Model Runner
How to use bklim8/BK_auto_model with Docker Model Runner:
```
docker model run hf.co/bklim8/BK_auto_model:F16
```

Lemonade

How to use bklim8/BK_auto_model with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bklim8/BK_auto_model:F16

Run and chat with the model

lemonade run user.BK_auto_model-F16

List all available models

lemonade list

BK_auto_model : GGUF

This model was finetuned and converted to GGUF format using Unsloth.

Example usage:

For text only LLMs: llama-cli -hf bklim8/BK_auto_model --jinja
For multimodal models: llama-mtmd-cli -hf bklim8/BK_auto_model --jinja

Available Model files:

gemma-4-e2b-it.Q4_K_M.gguf
gemma-4-e2b-it.F16-mmproj.gguf

⚠️ Ollama Note for Vision Models

Important: Ollama currently does not support separate mmproj files for vision models.

To create an Ollama model from this vision model:

Place the Modelfile in the same directory as the finetuned bf16 merged model
Run: ollama create model_name -f ./Modelfile (Replace model_name with your desired name)

This will create a unified bf16 model that Ollama can use. This was trained 2x faster with Unsloth

Downloads last month: 2

GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

4-bit

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