Instructions to use RANDMEDIATION/DiogenialRAG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use RANDMEDIATION/DiogenialRAG with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("RANDMEDIATION/DiogenialRAG") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use RANDMEDIATION/DiogenialRAG with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "RANDMEDIATION/DiogenialRAG" --prompt "Once upon a time"
- Atomic Chat
- Xet hash:
- 7d2923dbfbe5218d421c0f1cac1725e7331901d8f8a2e20ed92e5fed1ab998e9
- Size of remote file:
- 967 Bytes
- SHA256:
- 747ec99bae3fd9d7bf9c3b9172e5ca0d82ca0d23d64a58ef1752b612f196817b
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