Instructions to use j0no12/nero-optimizer-work-simow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use j0no12/nero-optimizer-work-simow 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-simow") 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-simow 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-simow" --prompt "Once upon a time"
- Atomic Chat
Download model.npz from j0no12/nero-optimizer-work-simow: direct link, hf CLI and curl.
- Browser
- Download file 4.01 MB
-
https://huggingface.co/j0no12/nero-optimizer-work-simow/resolve/main/model.npz
- Command line
-
hf download hf://j0no12/nero-optimizer-work-simow/model.npz
-
curl -L -o model.npz https://huggingface.co/j0no12/nero-optimizer-work-simow/resolve/main/model.npz
4.01 MB
- Xet hash:
- 60d24f00681140c37c96a7a5ff206ee302df9b4b069eff25368bc10c20fa3be7
- Size of remote file:
- 4.01 MB
- SHA256:
- f15e4bcb4467786dd1f791758c21014a0a3330ebfde5a1ad5838238ebe938eb8
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