Instructions to use pipenetwork/DeepSeek-V4-Flash-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use pipenetwork/DeepSeek-V4-Flash-MLX-8bit 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("pipenetwork/DeepSeek-V4-Flash-MLX-8bit") 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 pipenetwork/DeepSeek-V4-Flash-MLX-8bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "pipenetwork/DeepSeek-V4-Flash-MLX-8bit" --prompt "Once upon a time"
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{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Hello"
},
{
"role": "assistant",
"reasoning_content": "The user said hello, I should greet back.",
"content": "Hi there! How can I help you?"
},
{
"role": "user",
"content": "What is the capital of France?"
},
{
"role": "assistant",
"reasoning_content": "The user asks about the capital of France. It is Paris.",
"content": "The capital of France is Paris."
}
] |