Instructions to use whisperstream/stream-1-mini 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 whisperstream/stream-1-mini 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 whisperstream/stream-1-mini:F16 # Run inference directly in the terminal: llama cli -hf whisperstream/stream-1-mini:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf whisperstream/stream-1-mini:F16 # Run inference directly in the terminal: llama cli -hf whisperstream/stream-1-mini:F16
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 whisperstream/stream-1-mini:F16 # Run inference directly in the terminal: ./llama-cli -hf whisperstream/stream-1-mini:F16
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 whisperstream/stream-1-mini:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf whisperstream/stream-1-mini:F16
Use Docker
docker model run hf.co/whisperstream/stream-1-mini:F16
- Llama new
- LM Studio
- Jan
- vLLM
How to use whisperstream/stream-1-mini with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "whisperstream/stream-1-mini" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "whisperstream/stream-1-mini", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/whisperstream/stream-1-mini:F16
- Ollama
How to use whisperstream/stream-1-mini with Ollama:
ollama run hf.co/whisperstream/stream-1-mini:F16
- Unsloth Desktop
- Docker Model Runner
How to use whisperstream/stream-1-mini with Docker Model Runner:
docker model run hf.co/whisperstream/stream-1-mini:F16
- Lemonade
How to use whisperstream/stream-1-mini with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull whisperstream/stream-1-mini:F16
Run and chat with the model
lemonade run user.stream-1-mini-F16
List all available models
lemonade list
- Atomic Chat
Download tokenizer.json from whisperstream/stream-1-mini: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/whisperstream/stream-1-mini/resolve/main/tokenizer.json
- Command line
-
hf download hf://whisperstream/stream-1-mini/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/whisperstream/stream-1-mini/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- dde3c5a4d468da6fe61f11ffa8f04af2d101f8f4a0fe118c37bd31bb8c3017b6
- Size of remote file:
- 11.4 MB
- SHA256:
- be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.