Instructions to use TechCarbasa/MyModels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use TechCarbasa/MyModels with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="TechCarbasa/MyModels", filename="workspace/ComfyUI/models/WanVideo/Wan2.1-single/wan2.1-i2v-14b-480p-Q5_0.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 TechCarbasa/MyModels with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf TechCarbasa/MyModels:Q5_0 # Run inference directly in the terminal: llama-cli -hf TechCarbasa/MyModels:Q5_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf TechCarbasa/MyModels:Q5_0 # Run inference directly in the terminal: llama-cli -hf TechCarbasa/MyModels:Q5_0
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 TechCarbasa/MyModels:Q5_0 # Run inference directly in the terminal: ./llama-cli -hf TechCarbasa/MyModels:Q5_0
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 TechCarbasa/MyModels:Q5_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf TechCarbasa/MyModels:Q5_0
Use Docker
docker model run hf.co/TechCarbasa/MyModels:Q5_0
- LM Studio
- Jan
- Ollama
How to use TechCarbasa/MyModels with Ollama:
ollama run hf.co/TechCarbasa/MyModels:Q5_0
- Unsloth Studio
How to use TechCarbasa/MyModels 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 TechCarbasa/MyModels 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 TechCarbasa/MyModels to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for TechCarbasa/MyModels to start chatting
- Pi
How to use TechCarbasa/MyModels with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf TechCarbasa/MyModels:Q5_0
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "TechCarbasa/MyModels:Q5_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use TechCarbasa/MyModels with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf TechCarbasa/MyModels:Q5_0
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default TechCarbasa/MyModels:Q5_0
Run Hermes
hermes
- Docker Model Runner
How to use TechCarbasa/MyModels with Docker Model Runner:
docker model run hf.co/TechCarbasa/MyModels:Q5_0
- Lemonade
How to use TechCarbasa/MyModels with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull TechCarbasa/MyModels:Q5_0
Run and chat with the model
lemonade run user.MyModels-Q5_0
List all available models
lemonade list
| { | |
| "_name_or_path": "jonathandinu/face-parsing", | |
| "architectures": [ | |
| "SegformerForSemanticSegmentation" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "classifier_dropout_prob": 0.1, | |
| "decoder_hidden_size": 768, | |
| "depths": [ | |
| 3, | |
| 6, | |
| 40, | |
| 3 | |
| ], | |
| "downsampling_rates": [ | |
| 1, | |
| 4, | |
| 8, | |
| 16 | |
| ], | |
| "drop_path_rate": 0.1, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_sizes": [ | |
| 64, | |
| 128, | |
| 320, | |
| 512 | |
| ], | |
| "id2label": { | |
| "0": "background", | |
| "1": "skin", | |
| "2": "nose", | |
| "3": "eye_g", | |
| "4": "l_eye", | |
| "5": "r_eye", | |
| "6": "l_brow", | |
| "7": "r_brow", | |
| "8": "l_ear", | |
| "9": "r_ear", | |
| "10": "mouth", | |
| "11": "u_lip", | |
| "12": "l_lip", | |
| "13": "hair", | |
| "14": "hat", | |
| "15": "ear_r", | |
| "16": "neck_l", | |
| "17": "neck", | |
| "18": "cloth" | |
| }, | |
| "image_size": 224, | |
| "initializer_range": 0.02, | |
| "label2id": { | |
| "background": 0, | |
| "skin": 1, | |
| "nose": 2, | |
| "eye_g": 3, | |
| "l_eye": 4, | |
| "r_eye": 5, | |
| "l_brow": 6, | |
| "r_brow": 7, | |
| "l_ear": 8, | |
| "r_ear": 9, | |
| "mouth": 10, | |
| "u_lip": 11, | |
| "l_lip": 12, | |
| "hair": 13, | |
| "hat": 14, | |
| "ear_r": 15, | |
| "neck_l": 16, | |
| "neck": 17, | |
| "cloth": 18 | |
| }, | |
| "layer_norm_eps": 1e-06, | |
| "mlp_ratios": [ | |
| 4, | |
| 4, | |
| 4, | |
| 4 | |
| ], | |
| "model_type": "segformer", | |
| "num_attention_heads": [ | |
| 1, | |
| 2, | |
| 5, | |
| 8 | |
| ], | |
| "num_channels": 3, | |
| "num_encoder_blocks": 4, | |
| "patch_sizes": [ | |
| 7, | |
| 3, | |
| 3, | |
| 3 | |
| ], | |
| "reshape_last_stage": true, | |
| "semantic_loss_ignore_index": 255, | |
| "sr_ratios": [ | |
| 8, | |
| 4, | |
| 2, | |
| 1 | |
| ], | |
| "strides": [ | |
| 4, | |
| 2, | |
| 2, | |
| 2 | |
| ], | |
| "transformers_version": "4.37.0.dev0" | |
| } | |