roneneldan/TinyStories
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How to use navthings/tale with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf navthings/tale:F16 # Run inference directly in the terminal: llama cli -hf navthings/tale:F16
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf navthings/tale:F16 # Run inference directly in the terminal: llama cli -hf navthings/tale:F16
# 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 navthings/tale:F16 # Run inference directly in the terminal: ./llama-cli -hf navthings/tale:F16
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 navthings/tale:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf navthings/tale:F16
docker model run hf.co/navthings/tale:F16
How to use navthings/tale with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "navthings/tale"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "navthings/tale",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/navthings/tale:F16
How to use navthings/tale with Ollama:
ollama run hf.co/navthings/tale:F16
How to use navthings/tale with Docker Model Runner:
docker model run hf.co/navthings/tale:F16
How to use navthings/tale with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull navthings/tale:F16
lemonade run user.tale-F16
lemonade list
a ~47m parameter llama-style model trained from scratch on tinystories. i made it because my little brother kept asking me for bedtime stories.
ollama run navthings/tale
or use the gguf with llama.cpp:
llama-cli -m tale-q8_0.gguf -p "once upon a time "
or try it in your browser: https://navthings.github.io/playground/
| file | notes |
|---|---|
tale-f16.gguf |
unquantized |
tale-q8_0.gguf |
half the size, basically the same |
13 layers, 384 wide, 6 query heads, 2 kv heads, 384 token context. trained on apple silicon with a warmup + cosine lr schedule. this release has only done 2000 steps so its early.
8-bit
16-bit