Instructions to use mradermacher/Holo1-7B-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/Holo1-7B-i1-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/Holo1-7B-i1-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/Holo1-7B-i1-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 mradermacher/Holo1-7B-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/Holo1-7B-i1-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mradermacher/Holo1-7B-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/Holo1-7B-i1-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 mradermacher/Holo1-7B-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/Holo1-7B-i1-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 mradermacher/Holo1-7B-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/Holo1-7B-i1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/Holo1-7B-i1-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/Holo1-7B-i1-GGUF with Ollama:
ollama run hf.co/mradermacher/Holo1-7B-i1-GGUF:Q4_K_M
- Unsloth Studio
How to use mradermacher/Holo1-7B-i1-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 mradermacher/Holo1-7B-i1-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 mradermacher/Holo1-7B-i1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mradermacher/Holo1-7B-i1-GGUF to start chatting
- Docker Model Runner
How to use mradermacher/Holo1-7B-i1-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/Holo1-7B-i1-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/Holo1-7B-i1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/Holo1-7B-i1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Holo1-7B-i1-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Running inference on local images
I am new to this. I have been trying but could not make the the model answer on images.
from llama_cpp import Llama
import torch
from PIL import Image
import base64
llm = Llama(
model_path='Holo1-7B.i1-Q5_K_M.gguf',
n_gpu_layers=-1,
)
def image_to_base64_data_uri(file_path):
with open(file_path, "rb") as img_file:
base64_data = base64.b64encode(img_file.read()).decode('utf-8')
return f"data:image/png;base64,{base64_data}"
# Replace 'file_path.png' with the actual path to your PNG file
file_path = 'academic.png'
image= image_to_base64_data_uri(file_path)
messages = [
{"role": "system", "content": "You are an assistant who perfectly describes images."},
{
"role": "user",
"content": [
{"type": "image", "image": {"url": image}},
{"type" : "text", "text": "Describe this image in detail please."}
]
}
]
response = llm.create_chat_completion(messages)
print(response)
Output is :
{'id': 'chatcmpl-7b3fac95-4fc1-4d1c-a89e-b536331c3f57', 'object': 'chat.completion', 'created': 1749274274, 'model': 'Holo1-7B.i1-Q5_K_M.gguf', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': "The image shows a person with short, light brown hair wearing a white t-shirt with a graphic design on the front. The design appears to be a stylized illustration or logo. The person is standing against a plain, light-colored background. The lighting is bright, highlighting the person's features and the details of the t-shirt design. The overall style is casual and modern."}, 'logprobs': None, 'finish_reason': 'stop'}], 'usage': {'prompt_tokens': 32, 'completion_tokens': 75, 'total_tokens': 107}}
This is output is garbage. Its output description doesn't represent the image. What am I doing wrong? Please help.
You are not even loading the MMPROJ file containing the vision layers. As long you are not loading the MMPROJ file the model lacks any vision capabilities. Please download either https://huggingface.co/mradermacher/Holo1-7B-GGUF/blob/main/Holo1-7B.mmproj-f16.gguf or https://huggingface.co/mradermacher/Holo1-7B-GGUF/blob/main/Holo1-7B.mmproj-Q8_0.gguf and load it together with the GGUF containing the LLM layer for multimodal capabilities.
Ok I get that I need to also load the mmproj file for vision capabilities but how do I load it with the model in llama-cpp-python? I see mmproj files are loaded as chat handlers but there is no chat handler for this model. A sample code would be greatly appreciated. Thank you!