Instructions to use Aliguinga01/rule_violation2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Aliguinga01/rule_violation2 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 Aliguinga01/rule_violation2:F16 # Run inference directly in the terminal: llama cli -hf Aliguinga01/rule_violation2:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Aliguinga01/rule_violation2:F16 # Run inference directly in the terminal: llama cli -hf Aliguinga01/rule_violation2: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 Aliguinga01/rule_violation2:F16 # Run inference directly in the terminal: ./llama-cli -hf Aliguinga01/rule_violation2: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 Aliguinga01/rule_violation2:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Aliguinga01/rule_violation2:F16
Use Docker
docker model run hf.co/Aliguinga01/rule_violation2:F16
- LM Studio
- Jan
- Ollama
How to use Aliguinga01/rule_violation2 with Ollama:
ollama run hf.co/Aliguinga01/rule_violation2:F16
- Unsloth Studio
How to use Aliguinga01/rule_violation2 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 Aliguinga01/rule_violation2 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 Aliguinga01/rule_violation2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Aliguinga01/rule_violation2 to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Aliguinga01/rule_violation2 with Docker Model Runner:
docker model run hf.co/Aliguinga01/rule_violation2:F16
- Lemonade
How to use Aliguinga01/rule_violation2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Aliguinga01/rule_violation2:F16
Run and chat with the model
lemonade run user.rule_violation2-F16
List all available models
lemonade list
| #!/usr/bin/env python3 | |
| import argparse | |
| import os | |
| import json | |
| from safetensors import safe_open | |
| from collections import defaultdict | |
| parser = argparse.ArgumentParser(description='Process model with specified path') | |
| parser.add_argument('--model-path', '-m', help='Path to the model') | |
| args = parser.parse_args() | |
| model_path = os.environ.get('MODEL_PATH', args.model_path) | |
| if model_path is None: | |
| parser.error("Model path must be specified either via --model-path argument or MODEL_PATH environment variable") | |
| # Check if there's an index file (multi-file model) | |
| index_path = os.path.join(model_path, "model.safetensors.index.json") | |
| single_file_path = os.path.join(model_path, "model.safetensors") | |
| if os.path.exists(index_path): | |
| # Multi-file model | |
| print("Multi-file model detected") | |
| with open(index_path, 'r') as f: | |
| index_data = json.load(f) | |
| # Get the weight map (tensor_name -> file_name) | |
| weight_map = index_data.get("weight_map", {}) | |
| # Group tensors by file for efficient processing | |
| file_tensors = defaultdict(list) | |
| for tensor_name, file_name in weight_map.items(): | |
| file_tensors[file_name].append(tensor_name) | |
| print("Tensors in model:") | |
| # Process each shard file | |
| for file_name, tensor_names in file_tensors.items(): | |
| file_path = os.path.join(model_path, file_name) | |
| print(f"\n--- From {file_name} ---") | |
| with safe_open(file_path, framework="pt") as f: | |
| for tensor_name in sorted(tensor_names): | |
| tensor = f.get_tensor(tensor_name) | |
| print(f"- {tensor_name} : shape = {tensor.shape}, dtype = {tensor.dtype}") | |
| elif os.path.exists(single_file_path): | |
| # Single file model (original behavior) | |
| print("Single-file model detected") | |
| with safe_open(single_file_path, framework="pt") as f: | |
| keys = f.keys() | |
| print("Tensors in model:") | |
| for key in sorted(keys): | |
| tensor = f.get_tensor(key) | |
| print(f"- {key} : shape = {tensor.shape}, dtype = {tensor.dtype}") | |
| else: | |
| print(f"Error: Neither 'model.safetensors.index.json' nor 'model.safetensors' found in {model_path}") | |
| print("Available files:") | |
| if os.path.exists(model_path): | |
| for item in sorted(os.listdir(model_path)): | |
| print(f" {item}") | |
| else: | |
| print(f" Directory {model_path} does not exist") | |
| exit(1) | |