Instructions to use Nazmiven/muse_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Nazmiven/muse_model with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Nazmiven/muse_model", filename="Dirty-Muse-Writer-v01-Uncensored-Erotica-NSFW.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 Settings
- llama.cpp
How to use Nazmiven/muse_model with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Nazmiven/muse_model:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Nazmiven/muse_model:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Nazmiven/muse_model:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Nazmiven/muse_model: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 Nazmiven/muse_model:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Nazmiven/muse_model: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 Nazmiven/muse_model:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Nazmiven/muse_model:Q4_K_M
Use Docker
docker model run hf.co/Nazmiven/muse_model:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Nazmiven/muse_model with Ollama:
ollama run hf.co/Nazmiven/muse_model:Q4_K_M
- Unsloth Studio
How to use Nazmiven/muse_model 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 Nazmiven/muse_model 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 Nazmiven/muse_model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Nazmiven/muse_model to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Nazmiven/muse_model with Docker Model Runner:
docker model run hf.co/Nazmiven/muse_model:Q4_K_M
- Lemonade
How to use Nazmiven/muse_model with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Nazmiven/muse_model:Q4_K_M
Run and chat with the model
lemonade run user.muse_model-Q4_K_M
List all available models
lemonade list
File size: 2,121 Bytes
518d2a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | {
"architectures": [
"Gemma2ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"attn_logit_softcapping": 50.0,
"bos_token_id": 2,
"cache_implementation": "hybrid",
"torch_dtype": "bfloat16",
"eos_token_id": 1,
"final_logit_softcapping": 30.0,
"head_dim": 256,
"hidden_act": "gelu_pytorch_tanh",
"hidden_activation": "gelu_pytorch_tanh",
"hidden_size": 3584,
"initializer_range": 0.02,
"intermediate_size": 14336,
"layer_types": [
"sliding_attention",
"full_attention",
"sliding_attention",
"full_attention",
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"full_attention",
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"full_attention",
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],
"max_position_embeddings": 8192,
"model_type": "gemma2",
"num_attention_heads": 16,
"num_hidden_layers": 42,
"num_key_value_heads": 8,
"pad_token_id": 0,
"query_pre_attn_scalar": 256,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 10000.0,
"sliding_window": 4096,
"sliding_window_size": 4096,
"unsloth_version": "2026.3.11",
"use_cache": true,
"vocab_size": 256000
} |