Instructions to use j0no12/nero-optimizer-work-simo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use j0no12/nero-optimizer-work-simo 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("j0no12/nero-optimizer-work-simo") 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 j0no12/nero-optimizer-work-simo with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "j0no12/nero-optimizer-work-simo" --prompt "Once upon a time"
- Atomic Chat
File size: 230 Bytes
6654d17 | 1 2 3 4 5 6 7 8 9 | {
"optimizer": "simo",
"backend": "mlx",
"manifest": "/Users/jonah/Documents/ChatGPT/Training/data/prepared/finephrase-balanced-500m-2k-v2/manifest.json",
"context": 128,
"target_tokens": 500000000,
"batch_size": 32
}
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