Text Generation
MLX
Safetensors
English
rodan-modern
rodan
tiny-language-model
apple-silicon
byte-bpe
Instructions to use bfuzzy1/Rodan-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use bfuzzy1/Rodan-Base 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-Base") 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-Base 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-Base" --prompt "Once upon a time"
Upload README.md with huggingface_hub
Browse files
README.md
CHANGED
|
@@ -6,8 +6,8 @@ that actually holds up for its size, scored on how much it gets per parameter ra
|
|
| 6 |
| Model | Stage | Purpose |
|
| 7 |
|---|---|---|
|
| 8 |
| **Rodan-10M-Base** | pretraining | foundation: commonsense + knowledge |
|
| 9 |
-
| Rodan-10M-Chat *(
|
| 10 |
-
| Rodan-10M-Reasoning *(
|
| 11 |
| Rodan-10M-Latent *(planned)* | latent reasoning | in-head compute, no CoT tokens |
|
| 12 |
|
| 13 |
This card covers the base model only. The chat, reasoning, and latent stages are separate models with their
|
|
|
|
| 6 |
| Model | Stage | Purpose |
|
| 7 |
|---|---|---|
|
| 8 |
| **Rodan-10M-Base** | pretraining | foundation: commonsense + knowledge |
|
| 9 |
+
| Rodan-10M-Chat *(released)* | instruction fold | chat / instruction following |
|
| 10 |
+
| Rodan-10M-Reasoning *(released)* | recursive depth + CoT fold + DPO | verifiable math + reasoning |
|
| 11 |
| Rodan-10M-Latent *(planned)* | latent reasoning | in-head compute, no CoT tokens |
|
| 12 |
|
| 13 |
This card covers the base model only. The chat, reasoning, and latent stages are separate models with their
|