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.model from ikppramesh/irx-mini: direct link, hf CLI and curl.
- Browser
- Download file 4.69 MB
-
https://huggingface.co/ikppramesh/irx-mini/resolve/main/tokenizer.model
- Command line
-
hf download hf://ikppramesh/irx-mini/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/ikppramesh/irx-mini/resolve/main/tokenizer.model
4.69 MB
- Xet hash:
- f6aa6b6d77b66c0b5905517df9e5261c32195192839dd05e71e37d6b89933074
- Size of remote file:
- 4.69 MB
- SHA256:
- 1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
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