Instructions to use ChristianAzinn/tiny-json with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ChristianAzinn/tiny-json with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ChristianAzinn/tiny-json",
	filename="json_qwen2_0.5b_rev1_Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ChristianAzinn/tiny-json with llama.cpp:

Install from brew

brew 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_0

Install 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_0

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 ChristianAzinn/tiny-json:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf ChristianAzinn/tiny-json:Q8_0

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 ChristianAzinn/tiny-json:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ChristianAzinn/tiny-json:Q8_0

Use Docker

docker model run hf.co/ChristianAzinn/tiny-json:Q8_0

LM Studio
Jan
Ollama
How to use ChristianAzinn/tiny-json with Ollama:
```
ollama run hf.co/ChristianAzinn/tiny-json:Q8_0
```

Unsloth Studio new

How to use ChristianAzinn/tiny-json 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 ChristianAzinn/tiny-json 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 ChristianAzinn/tiny-json to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ChristianAzinn/tiny-json to start chatting

Docker Model Runner
How to use ChristianAzinn/tiny-json with Docker Model Runner:
```
docker model run hf.co/ChristianAzinn/tiny-json:Q8_0
```

Lemonade

How to use ChristianAzinn/tiny-json with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ChristianAzinn/tiny-json:Q8_0

Run and chat with the model

lemonade run user.tiny-json-Q8_0

List all available models

lemonade list

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).

Downloads last month: 36

GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

8-bit

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