Instructions to use mlx-community/DeepSeek-V3.2_bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/DeepSeek-V3.2_bf16 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("mlx-community/DeepSeek-V3.2_bf16") 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 mlx-community/DeepSeek-V3.2_bf16 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "mlx-community/DeepSeek-V3.2_bf16" --prompt "Once upon a time"
- Atomic Chat
- Xet hash:
- ea567142896c4918b3259e7a4977db64ba00af1615621319c3cc576018afd75e
- Size of remote file:
- 7.52 GB
- SHA256:
- 70b73bfa3a89cdd5239ea4054d934f1aa34f545c9d6f5061e5236c3106c30e76
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.