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lemuralabs/DeepSeek-V4-Flash-5bit-mlx
DeepSeek-V4-Flash-5bit-mlx is a text generation model from lemuralabs. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as mit.
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Downloads · 30 days
115
4% of all-time downloads
All-time downloads
2.9K
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284B
196 GB on disk
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.safetensors196 GB · 100%
How the weights are stored.
U32284B · 100%
From the Hugging Face model README
lemuralabs/DeepSeek-V4-Flash-5bit-mlx is an Apple-Silicon MLX quantization of deepseek-ai/DeepSeek-V4-Flash.
No fine-tuning, distillation, or retraining was applied. The official mixed FP4/FP8 source weights were converted locally, the MTP head was dropped because it is not used for normal decode, and router/mHC/control tensors were preserved rather than aggressively quantized.
| Property | Value |
|---|---|
| Base model | deepseek-ai/DeepSeek-V4-Flash |
| Architecture | DeepSeek-V4 Flash MoE, 284B total / 13B active, 1M context |
| Local profile | MLX-Affine-Q5 |
| Bundle size | 195.67 GB |
| Layout | Pre-stacked MLX switch_mlp layout |
| MTP head | Dropped |
| Validation | Safetensors header/index validation, metadata validation |
| Tensor class | Codec | Bits / handling |
|---|---|---|
| Linear/Embedding/SwitchLinear weights | MLX affine | 5-bit, group size 64 |
| Routed experts | MLX affine | pre-stacked switch_mlp tensors with .weight, .scales, .biases |
| Norms, router gate, mHC, sinks, APE, integer routing tables | passthrough | source precision preserved |
This is a normal MLX affine quantization, not JANGTQ/TurboQuant. Quantized tensors use the standard MLX triplet layout:
.weight.scales.biasespip install -U mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("lemuralabs/DeepSeek-V4-Flash-5bit-mlx")
prompt = "Write a short note about MLX quantization."
text = generate(model, tokenizer, prompt=prompt, verbose=True)
print(text)
For the DeepSeek-V4 official chat/message format, see the included encoding/ folder from the upstream repository.
model-*.safetensors: standard MLX affine shardsmodel.safetensors.index.json: shard indexconfig.json, jang_config.json: MLX metadataencoding/: upstream DeepSeek-V4 prompt encoding referenceThe naming follows the common MLX category convention used by mlx-community/*-4bit / *-8bit uploads and the local Lemura Labs/*-6bit-mlx style, while the README keeps the explicit recipe/validation structure used by larger DeepSeek-V4 quant uploads.
MIT, following the upstream DeepSeek-V4-Flash release.