Instructions to use MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B",
	filename="unsloth.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B: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 MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B: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 MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B:Q4_K_M

Use Docker

docker model run hf.co/MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B:Q4_K_M

LM Studio
Jan
Ollama
How to use MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B with Ollama:
```
ollama run hf.co/MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B:Q4_K_M
```

Unsloth Studio new

How to use MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B 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 MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B 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 MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B to start chatting

Pi new

How to use MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B:Q4_K_M

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": "MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B:Q4_K_M

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 MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B with Docker Model Runner:
```
docker model run hf.co/MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B:Q4_K_M
```

Lemonade

How to use MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B:Q4_K_M

Run and chat with the model

lemonade run user.OCR-500Samples-Qwen-2.5-Coder-3B-Q4_K_M

List all available models

lemonade list

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

README.md exists but content is empty.

Downloads last month: -

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-Coder-3B

Quantized

(29)

this model

MarioCap
/

OCR-500Samples-Qwen-2.5-Coder-3B

You need to agree to share your contact information to access this model

Model tree for MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B

Dataset used to train MarioCap/OCR-500Samples-Qwen-2.5-Coder-3B