Text Generation
MLX
Safetensors
English
rodan-modern
rodan
tiny-language-model
reasoning
chain-of-thought
dpo
Instructions to use bfuzzy1/Rodan-Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use bfuzzy1/Rodan-Reasoning 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("bfuzzy1/Rodan-Reasoning") 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 bfuzzy1/Rodan-Reasoning with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "bfuzzy1/Rodan-Reasoning" --prompt "Once upon a time"
- Xet hash:
- 78794ba2dcb752c772d423f99e6882fd03baabc9159976aa512cca46c8979333
- Size of remote file:
- 20.8 MB
- SHA256:
- 242a15653c283c89c0b1834a775eeaabf68ba07d53631c797904333faf90c248
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