Instructions to use cortexso/tinyllama 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 cortexso/tinyllama 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 cortexso/tinyllama:Q4_K_M # Run inference directly in the terminal: llama cli -hf cortexso/tinyllama:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf cortexso/tinyllama:Q4_K_M # Run inference directly in the terminal: llama cli -hf cortexso/tinyllama: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 cortexso/tinyllama:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf cortexso/tinyllama: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 cortexso/tinyllama:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf cortexso/tinyllama:Q4_K_M
Use Docker
docker model run hf.co/cortexso/tinyllama:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use cortexso/tinyllama with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cortexso/tinyllama" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cortexso/tinyllama", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cortexso/tinyllama:Q4_K_M
- Ollama
How to use cortexso/tinyllama with Ollama:
ollama run hf.co/cortexso/tinyllama:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use cortexso/tinyllama with Docker Model Runner:
docker model run hf.co/cortexso/tinyllama:Q4_K_M
- Lemonade
How to use cortexso/tinyllama with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cortexso/tinyllama:Q4_K_M
Run and chat with the model
lemonade run user.tinyllama-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download tinyllama-1.1b-chat-v1.0-q3_k_l.gguf from cortexso/tinyllama: direct link, hf CLI and curl.
- Browser
- Download file 592 MB
-
https://huggingface.co/cortexso/tinyllama/resolve/main/tinyllama-1.1b-chat-v1.0-q3_k_l.gguf
- Command line
-
hf download hf://cortexso/tinyllama/tinyllama-1.1b-chat-v1.0-q3_k_l.gguf
-
curl -L -o tinyllama-1.1b-chat-v1.0-q3_k_l.gguf https://huggingface.co/cortexso/tinyllama/resolve/main/tinyllama-1.1b-chat-v1.0-q3_k_l.gguf
592 MB
- Xet hash:
- 59c44c529af6cfae9a6daa611ca7ce760c1843a9bc5d428de7ff034757cfa075
- Size of remote file:
- 592 MB
- SHA256:
- ac85193e7af88af22f14507954e25bc6cceba198a2fd5e6a182ab784892beb22
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.