Instructions to use Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF", filename="stablelm-zephyr-3b-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 Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Tien-THM/stablelm-zephyr-3b-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-server -hf Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Tien-THM/stablelm-zephyr-3b-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 Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Tien-THM/stablelm-zephyr-3b-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 Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF with Ollama:
ollama run hf.co/Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF:Q4_K_M
- Unsloth Studio
How to use Tien-THM/stablelm-zephyr-3b-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 Tien-THM/stablelm-zephyr-3b-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 Tien-THM/stablelm-zephyr-3b-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 Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF with Docker Model Runner:
docker model run hf.co/Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF:Q4_K_M
- Lemonade
How to use Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.stablelm-zephyr-3b-Q4_K_M-GGUF-Q4_K_M
List all available models
lemonade list
Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF
This model was converted to GGUF format from stabilityai/stablelm-zephyr-3b 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 Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF --hf-file stablelm-zephyr-3b-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF --hf-file stablelm-zephyr-3b-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 Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF --hf-file stablelm-zephyr-3b-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF --hf-file stablelm-zephyr-3b-q4_k_m.gguf -c 2048
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Model tree for Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF
Base model
stabilityai/stablelm-zephyr-3bDatasets used to train Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF
meta-math/MetaMathQA
HuggingFaceH4/ultrafeedback_binarized
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard46.080
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard74.160
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard46.170
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard46.490
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard65.510
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard42.150
docker model run hf.co/Tien-THM/stablelm-zephyr-3b-Q4_K_M-GGUF:Q4_K_M