Instructions to use ikppramesh/irx-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ikppramesh/irx-mini with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("ikppramesh/irx-mini") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use ikppramesh/irx-mini with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "ikppramesh/irx-mini"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "ikppramesh/irx-mini" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ikppramesh/irx-mini", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
Download tokenizer.json from ikppramesh/irx-mini: direct link, hf CLI and curl.
- Browser
- Download file 33.4 MB
-
https://huggingface.co/ikppramesh/irx-mini/resolve/main/tokenizer.json
- Command line
-
hf download hf://ikppramesh/irx-mini/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ikppramesh/irx-mini/resolve/main/tokenizer.json
33.4 MB
- Xet hash:
- 23db2e055d80cccf03598ded35b611c37cce36e1bc0bf93b87209e2a480a073d
- Size of remote file:
- 33.4 MB
- SHA256:
- daab2354f8a74e70d70b4d1f804939b68a8c9624dd06cb7858e52dd8970e9726
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