How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ChristianAzinn/tiny-json:Q8_0# Run inference directly in the terminal:
llama-cli -hf ChristianAzinn/tiny-json:Q8_0Use 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 ChristianAzinn/tiny-json:Q8_0# Run inference directly in the terminal:
./llama-cli -hf ChristianAzinn/tiny-json:Q8_0Build 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 ChristianAzinn/tiny-json:Q8_0# Run inference directly in the terminal:
./build/bin/llama-cli -hf ChristianAzinn/tiny-json:Q8_0Use Docker
docker model run hf.co/ChristianAzinn/tiny-json:Q8_0Quick Links
TinyJSON
Trained on my json-training dataset,
these are finetunes of the smallest state-of-the-art LLMs to output in structured JSON.
Where their base/instruct versions have so little clue how to output JSON that forcing it using techniques like grammars simply hangs forever, these little guys (mostly) work like a charm. (SmolLM 135M still sometimes babbles on. Set a maximum token limit.)
Training was done with Unsloth at 4bit (lmao), rank=8, alpha=8, for 3 epochs each.
rev1 models were trained on the first revision (11.6k rows) of json-training,
while rev2 models were trained on the second (20.6k rows).
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Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf ChristianAzinn/tiny-json:Q8_0# Run inference directly in the terminal: llama-cli -hf ChristianAzinn/tiny-json:Q8_0