Instructions to use Linov1991/linov-ocr-breastcancer-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Linov1991/linov-ocr-breastcancer-finetune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Linov1991/linov-ocr-breastcancer-finetune") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("Linov1991/linov-ocr-breastcancer-finetune") model = AutoModelForMultimodalLM.from_pretrained("Linov1991/linov-ocr-breastcancer-finetune", device_map="auto") - llama-cpp-python
How to use Linov1991/linov-ocr-breastcancer-finetune with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Linov1991/linov-ocr-breastcancer-finetune", filename="LINOV-OCR_K_M.gguf", )
llm.create_chat_completion( messages = "\"cats.jpg\"" )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Linov1991/linov-ocr-breastcancer-finetune 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 Linov1991/linov-ocr-breastcancer-finetune # Run inference directly in the terminal: llama cli -hf Linov1991/linov-ocr-breastcancer-finetune
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Linov1991/linov-ocr-breastcancer-finetune # Run inference directly in the terminal: llama cli -hf Linov1991/linov-ocr-breastcancer-finetune
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 Linov1991/linov-ocr-breastcancer-finetune # Run inference directly in the terminal: ./llama-cli -hf Linov1991/linov-ocr-breastcancer-finetune
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 Linov1991/linov-ocr-breastcancer-finetune # Run inference directly in the terminal: ./build/bin/llama-cli -hf Linov1991/linov-ocr-breastcancer-finetune
Use Docker
docker model run hf.co/Linov1991/linov-ocr-breastcancer-finetune
- LM Studio
- Jan
- Ollama
How to use Linov1991/linov-ocr-breastcancer-finetune with Ollama:
ollama run hf.co/Linov1991/linov-ocr-breastcancer-finetune
- Unsloth Studio
How to use Linov1991/linov-ocr-breastcancer-finetune 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 Linov1991/linov-ocr-breastcancer-finetune 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 Linov1991/linov-ocr-breastcancer-finetune to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Linov1991/linov-ocr-breastcancer-finetune to start chatting
- Pi
How to use Linov1991/linov-ocr-breastcancer-finetune with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Linov1991/linov-ocr-breastcancer-finetune
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": "Linov1991/linov-ocr-breastcancer-finetune" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Linov1991/linov-ocr-breastcancer-finetune with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Linov1991/linov-ocr-breastcancer-finetune
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 Linov1991/linov-ocr-breastcancer-finetune
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use Linov1991/linov-ocr-breastcancer-finetune with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Linov1991/linov-ocr-breastcancer-finetune
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 "Linov1991/linov-ocr-breastcancer-finetune" \ --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 Linov1991/linov-ocr-breastcancer-finetune with Docker Model Runner:
docker model run hf.co/Linov1991/linov-ocr-breastcancer-finetune
- Lemonade
How to use Linov1991/linov-ocr-breastcancer-finetune with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Linov1991/linov-ocr-breastcancer-finetune
Run and chat with the model
lemonade run user.linov-ocr-breastcancer-finetune-{{QUANT_TAG}}List all available models
lemonade list
Breast Cancer Ultrasound Image Classification Model
AI Prediction: benign Confidence: 82%
This result is generated by an AI model for research and educational purposes only. It is not a medical diagnosis. Please consult a qualified healthcare professional for clinical interpretation. A breast ultrasound image classification model fine-tuned to classify ultrasound images into three categories:
- Benign
- Malignant
- Normal
This model was trained using the breastcancer-ultrasound-images dataset and is intended for medical imaging research, education, prototyping, and local/offline AI experimentation.
This model can also be explored for mobile and on-device AI workflows, where ultrasound images are processed directly on a laptop, edge device, or supported mobile phone without relying on a cloud API.
Important: This model is not a medical diagnostic tool. It is intended for research, education, and prototype development only.
Model Overview
| Item | Description |
|---|---|
| Model task | Breast ultrasound image classification |
| Input | Breast ultrasound image |
| Output | Benign, malignant, or normal prediction |
| Modality | Medical image / ultrasound image |
| Dataset | emre570/breastcancer-ultrasound-images |
| Main use case | Medical imaging AI research and prototype development |
| Local usage | Supported through compatible local inference runtime |
| Mobile usage | Possible after conversion or integration into mobile runtime |
| Offline usage | Possible after model and runtime are installed locally |
| Clinical use | Not intended for clinical diagnosis |
What This Model Is For
This model is designed to classify breast ultrasound images into one of three categories:
benign
malignant
normal
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docker model run hf.co/Linov1991/linov-ocr-breastcancer-finetune