Text Generation
Transformers
Safetensors
cohere2_moe
conversational
chat
code
agent
heretic
uncensored
decensored
abliterated
Instructions to use OS-Software/North-Mini-Code-1.0-Uncensored-Heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OS-Software/North-Mini-Code-1.0-Uncensored-Heretic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OS-Software/North-Mini-Code-1.0-Uncensored-Heretic") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("OS-Software/North-Mini-Code-1.0-Uncensored-Heretic") model = AutoModelForCausalLM.from_pretrained("OS-Software/North-Mini-Code-1.0-Uncensored-Heretic", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use OS-Software/North-Mini-Code-1.0-Uncensored-Heretic with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OS-Software/North-Mini-Code-1.0-Uncensored-Heretic" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OS-Software/North-Mini-Code-1.0-Uncensored-Heretic", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OS-Software/North-Mini-Code-1.0-Uncensored-Heretic
- SGLang
How to use OS-Software/North-Mini-Code-1.0-Uncensored-Heretic with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "OS-Software/North-Mini-Code-1.0-Uncensored-Heretic" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OS-Software/North-Mini-Code-1.0-Uncensored-Heretic", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "OS-Software/North-Mini-Code-1.0-Uncensored-Heretic" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OS-Software/North-Mini-Code-1.0-Uncensored-Heretic", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use OS-Software/North-Mini-Code-1.0-Uncensored-Heretic with Docker Model Runner:
docker model run hf.co/OS-Software/North-Mini-Code-1.0-Uncensored-Heretic
Download tokenizer.json from OS-Software/North-Mini-Code-1.0-Uncensored-Heretic: direct link, hf CLI and curl.
- Browser
- Download file 19.6 MB
-
https://huggingface.co/OS-Software/North-Mini-Code-1.0-Uncensored-Heretic/resolve/main/tokenizer.json
- Command line
-
hf download hf://OS-Software/North-Mini-Code-1.0-Uncensored-Heretic/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/OS-Software/North-Mini-Code-1.0-Uncensored-Heretic/resolve/main/tokenizer.json
19.6 MB
- Xet hash:
- 118eab50efe9b840d3ef4eb741001bb308038e20d13540b6dce629839f80dc18
- Size of remote file:
- 19.6 MB
- SHA256:
- e7edbc26b2d1ab947f099b201892d2c0cb873a9ca8d4d3ffde2d82f912e44514
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