Instructions to use pipenetwork/DeepSeek-V4.1-Flash-MLX-mixed-4_8bit-engram6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use pipenetwork/DeepSeek-V4.1-Flash-MLX-mixed-4_8bit-engram6 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-V4.1-Flash-MLX-mixed-4_8bit-engram6") 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-V4.1-Flash-MLX-mixed-4_8bit-engram6 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-V4.1-Flash-MLX-mixed-4_8bit-engram6" --prompt "Once upon a time"
- Atomic Chat
| {"files": {"model-layers-12.safetensors": ["layers.12.attn.attn_sink", "layers.12.attn.kv_norm.weight", "layers.12.attn.q_norm.weight", "layers.12.attn.wkv.biases", "layers.12.attn.wkv.scales", "layers.12.attn.wkv.weight", "layers.12.attn.wo_a.weight", "layers.12.attn.wo_b.biases", "layers.12.attn.wo_b.scales", "layers.12.attn.wo_b.weight", "layers.12.attn.wq_a.biases", "layers.12.attn.wq_a.scales", "layers.12.attn.wq_a.weight", "layers.12.attn.wq_b.biases", "layers.12.attn.wq_b.scales", "layers.12.attn.wq_b.weight", "layers.12.attn_norm.weight", "layers.12.ffn.experts.down_proj.biases", "layers.12.ffn.experts.down_proj.scales", "layers.12.ffn.experts.down_proj.weight", "layers.12.ffn.experts.gate_proj.biases", "layers.12.ffn.experts.gate_proj.scales", "layers.12.ffn.experts.gate_proj.weight", "layers.12.ffn.experts.up_proj.biases", "layers.12.ffn.experts.up_proj.scales", "layers.12.ffn.experts.up_proj.weight", "layers.12.ffn.gate.bias", "layers.12.ffn.gate.bias_vl", "layers.12.ffn.gate.weight", "layers.12.ffn.shared_experts.w1.biases", "layers.12.ffn.shared_experts.w1.scales", "layers.12.ffn.shared_experts.w1.weight", "layers.12.ffn.shared_experts.w2.biases", "layers.12.ffn.shared_experts.w2.scales", "layers.12.ffn.shared_experts.w2.weight", "layers.12.ffn.shared_experts.w3.biases", "layers.12.ffn.shared_experts.w3.scales", "layers.12.ffn.shared_experts.w3.weight", "layers.12.ffn_norm.weight", "layers.12.hc_attn_base", "layers.12.hc_attn_fn", "layers.12.hc_attn_scale", "layers.12.hc_ffn_base", "layers.12.hc_ffn_fn", "layers.12.hc_ffn_scale"]}, "modules": {"layers.12.attn.wkv": {"group_size": 64, "bits": 8}, "layers.12.attn.wo_b": {"group_size": 64, "bits": 8}, "layers.12.attn.wq_a": {"group_size": 64, "bits": 8}, "layers.12.attn.wq_b": {"group_size": 64, "bits": 8}, "layers.12.ffn.shared_experts.w1": {"group_size": 64, "bits": 8}, "layers.12.ffn.shared_experts.w2": {"group_size": 64, "bits": 8}, "layers.12.ffn.shared_experts.w3": {"group_size": 64, "bits": 8}, "layers.12.ffn.experts.gate_proj": {"group_size": 64, "bits": 4}, "layers.12.ffn.experts.down_proj": {"group_size": 64, "bits": 4}, "layers.12.ffn.experts.up_proj": {"group_size": 64, "bits": 4}}} |