Instructions to use Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF 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 Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF 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 Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF: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 Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF: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 Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF with Ollama:
ollama run hf.co/Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF:Q4_K_M
- Unsloth Studio
How to use Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF 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 Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF 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 Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF with Docker Model Runner:
docker model run hf.co/Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF:Q4_K_M
- Lemonade
How to use Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.emotion_text_classifier-Q4_K_M-GGUF-Q4_K_M
List all available models
lemonade list
Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF
This model was converted to GGUF format from michelleli99/emotion_text_classifier using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF --hf-file emotion_text_classifier-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF --hf-file emotion_text_classifier-q4_k_m.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF --hf-file emotion_text_classifier-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Lyaaaaaaaaaaaaaaa/emotion_text_classifier-Q4_K_M-GGUF --hf-file emotion_text_classifier-q4_k_m.gguf -c 2048
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Base model
michelleli99/emotion_text_classifier