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
- 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
- Atomic Chat
| import pytest | |
| from utils import * | |
| import base64 | |
| import requests | |
| server: ServerProcess | |
| def get_img_url(id: str) -> str: | |
| IMG_URL_0 = "https://huggingface.co/ggml-org/tinygemma3-GGUF/resolve/main/test/11_truck.png" | |
| IMG_URL_1 = "https://huggingface.co/ggml-org/tinygemma3-GGUF/resolve/main/test/91_cat.png" | |
| if id == "IMG_URL_0": | |
| return IMG_URL_0 | |
| elif id == "IMG_URL_1": | |
| return IMG_URL_1 | |
| elif id == "IMG_BASE64_URI_0": | |
| response = requests.get(IMG_URL_0) | |
| response.raise_for_status() # Raise an exception for bad status codes | |
| return "data:image/png;base64," + base64.b64encode(response.content).decode("utf-8") | |
| elif id == "IMG_BASE64_0": | |
| response = requests.get(IMG_URL_0) | |
| response.raise_for_status() # Raise an exception for bad status codes | |
| return base64.b64encode(response.content).decode("utf-8") | |
| elif id == "IMG_BASE64_URI_1": | |
| response = requests.get(IMG_URL_1) | |
| response.raise_for_status() # Raise an exception for bad status codes | |
| return "data:image/png;base64," + base64.b64encode(response.content).decode("utf-8") | |
| elif id == "IMG_BASE64_1": | |
| response = requests.get(IMG_URL_1) | |
| response.raise_for_status() # Raise an exception for bad status codes | |
| return base64.b64encode(response.content).decode("utf-8") | |
| else: | |
| return id | |
| JSON_MULTIMODAL_KEY = "multimodal_data" | |
| JSON_PROMPT_STRING_KEY = "prompt_string" | |
| def create_server(): | |
| global server | |
| server = ServerPreset.tinygemma3() | |
| def test_models_supports_multimodal_capability(): | |
| global server | |
| server.start() | |
| res = server.make_request("GET", "/models", data={}) | |
| assert res.status_code == 200 | |
| model_info = res.body["models"][0] | |
| print(model_info) | |
| assert "completion" in model_info["capabilities"] | |
| assert "multimodal" in model_info["capabilities"] | |
| def test_v1_models_supports_multimodal_capability(): | |
| global server | |
| server.start() | |
| res = server.make_request("GET", "/v1/models", data={}) | |
| assert res.status_code == 200 | |
| model_info = res.body["models"][0] | |
| print(model_info) | |
| assert "completion" in model_info["capabilities"] | |
| assert "multimodal" in model_info["capabilities"] | |
| def test_vision_chat_completion(prompt, image_url, success, re_content): | |
| global server | |
| server.start() | |
| res = server.make_request("POST", "/chat/completions", data={ | |
| "temperature": 0.0, | |
| "top_k": 1, | |
| "messages": [ | |
| {"role": "user", "content": [ | |
| {"type": "text", "text": prompt}, | |
| {"type": "image_url", "image_url": { | |
| "url": get_img_url(image_url), | |
| }}, | |
| ]}, | |
| ], | |
| }) | |
| if success: | |
| assert res.status_code == 200 | |
| choice = res.body["choices"][0] | |
| assert "assistant" == choice["message"]["role"] | |
| assert match_regex(re_content, choice["message"]["content"]) | |
| else: | |
| assert res.status_code != 200 | |
| def test_vision_completion(prompt, image_data, success, re_content): | |
| global server | |
| server.start() | |
| res = server.make_request("POST", "/completions", data={ | |
| "temperature": 0.0, | |
| "top_k": 1, | |
| "prompt": { | |
| JSON_PROMPT_STRING_KEY: prompt, | |
| JSON_MULTIMODAL_KEY: [ get_img_url(image_data) ], | |
| }, | |
| }) | |
| if success: | |
| assert res.status_code == 200 | |
| content = res.body["content"] | |
| assert match_regex(re_content, content) | |
| else: | |
| assert res.status_code != 200 | |
| def test_vision_embeddings(prompt, image_data, success): | |
| global server | |
| server.server_embeddings = True | |
| server.n_batch = 512 | |
| server.start() | |
| image_data = get_img_url(image_data) | |
| res = server.make_request("POST", "/embeddings", data={ | |
| "content": [ | |
| { JSON_PROMPT_STRING_KEY: prompt, JSON_MULTIMODAL_KEY: [ image_data ] }, | |
| { JSON_PROMPT_STRING_KEY: prompt, JSON_MULTIMODAL_KEY: [ image_data ] }, | |
| { JSON_PROMPT_STRING_KEY: prompt, }, | |
| ], | |
| }) | |
| if success: | |
| assert res.status_code == 200 | |
| content = res.body | |
| # Ensure embeddings are stable when multimodal. | |
| assert content[0]['embedding'] == content[1]['embedding'] | |
| # Ensure embeddings without multimodal but same prompt do not match multimodal embeddings. | |
| assert content[0]['embedding'] != content[2]['embedding'] | |
| else: | |
| assert res.status_code != 200 | |