Text Generation
Transformers
Safetensors
murzik
feature-extraction
nullxes
causal-lm
custom_code
multilingual
conversational
Instructions to use MagistrTheOne/murzik-15b-init with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MagistrTheOne/murzik-15b-init with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MagistrTheOne/murzik-15b-init", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MagistrTheOne/murzik-15b-init", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use MagistrTheOne/murzik-15b-init with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MagistrTheOne/murzik-15b-init" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MagistrTheOne/murzik-15b-init", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/MagistrTheOne/murzik-15b-init
- SGLang
How to use MagistrTheOne/murzik-15b-init 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 "MagistrTheOne/murzik-15b-init" \ --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": "MagistrTheOne/murzik-15b-init", "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 "MagistrTheOne/murzik-15b-init" \ --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": "MagistrTheOne/murzik-15b-init", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use MagistrTheOne/murzik-15b-init with Docker Model Runner:
docker model run hf.co/MagistrTheOne/murzik-15b-init
| from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer | |
| from .configuration_murzik import MurzikConfig | |
| from .configuration_murzik_moe import MurzikMoeConfig | |
| from .modeling_murzik import MurzikForCausalLM | |
| from .modeling_murzik_moe import MurzikMoeForCausalLM | |
| from .tokenization_murzik import MurzikTokenizer | |
| AutoConfig.register("murzik", MurzikConfig) | |
| AutoConfig.register("murzik_moe", MurzikMoeConfig) | |
| AutoModelForCausalLM.register(MurzikConfig, MurzikForCausalLM) | |
| AutoModelForCausalLM.register(MurzikMoeConfig, MurzikMoeForCausalLM) | |
| AutoTokenizer.register(MurzikConfig, slow_tokenizer_class=MurzikTokenizer) | |
| __all__ = [ | |
| "MurzikConfig", | |
| "MurzikMoeConfig", | |
| "MurzikForCausalLM", | |
| "MurzikMoeForCausalLM", | |
| "MurzikTokenizer", | |
| ] | |