---
title: DeepSeek-V4.1-Flash-1017-oQ4e-mtp
canonical_url: "https://www.modelscope.cn/models/mlx-community/DeepSeek-V4.1-Flash-1017-oQ4e-mtp"
md_url: "https://www.modelscope.cn/models/mlx-community/DeepSeek-V4.1-Flash-1017-oQ4e-mtp.md"
repository: mlx-community/DeepSeek-V4.1-Flash-1017-oQ4e-mtp
last_updated: 2026-10-17
license: mit
pipeline_tag: image-text-to-text
tasks:
  - image-text-to-text
model_type:
  - deepseek_v41
architectures:
  - DeepseekV41ForCausalLM
base_model:
  - deepseek-ai/DeepSeek-V4.1-Flash-1017
base_model_relation: quantized
parameters: 66.9B
tensor_type:
  - U32
  - F32
  - U8
  - BF16
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 217
stars: 2
tags:
  - mlx
  - oq
  - quantized
  - deepseek_v41
  - mtp
---

# DeepSeek-V4.1-Flash-1017-oQ4e-mtp

> DeepSeek-V4.1-Flash-1017-oQ4e-mtp - mlx-community 在 ModelScope 开源的模型。DeepSeek-V4.1-Flash-1017-oQ4e-mtp

mlx-community/DeepSeek-V4.1-Flash-1017-oQ4e-mtp 是 ModelScope 魔搭社区上的 66.9B 参数image-text-to-text模型，采用 mit 许可，基于 deepseek-ai/DeepSeek-V4.1-Flash-1017 构建。

- **Repository**: mlx-community/DeepSeek-V4.1-Flash-1017-oQ4e-mtp
- **License**: mit
- **Tasks**: image-text-to-text
- **Parameters**: 66.9B
- **Base model**: deepseek-ai/DeepSeek-V4.1-Flash-1017
- **Tags**: mlx, oq, quantized, deepseek_v41, mtp
- **Downloads**: 217
- **Stars**: 2
- **Last updated**: 2026-10-17

Source: https://www.modelscope.cn/models/mlx-community/DeepSeek-V4.1-Flash-1017-oQ4e-mtp

---

# DeepSeek-V4.1-Flash-1017-oQ4e-mtp

This model was quantized using [oQ](https://github.com/jundot/omlx) (oMLX v0.7.1) mixed-precision quantization with Improved oQe Quantization ([jundot/omlx#4385](https://github.com/jundot/omlx/pull/4385)).

A mixed-precision MLX checkpoint of [deepseek-ai/DeepSeek-V4.1-Flash-1017](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017), a minor update to [DeepSeek-V4.1-Flash](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash) with improved reasoning, coding, agentic and visual performance. It is a multimodal mixture-of-experts model with 552B backbone parameters, a Causal Encoder-Decoder layout, Compressed Sparse Attention 2 (CSA2), Engram conditional memory and DSpark speculative decoding. It includes the language model, the Engram tables, the vision encoder, the multimodal aligner and the DSpark MTP weights.

The effective average is 4.365 bits/weight excluding the Engram tables, or 4.529 bits/weight for the complete checkpoint.

## Quantization and Bit Distribution

oQ4e keeps the original FP4 and FP8 projection precision, which already fits the budget, and quantizes only the Engram tables.

| Storage format | Logical weights | Share of total | Tensor storage | Effective bits/weight |
|---|---|---|---|---|
| MXFP4, group 32 | 557.171B | 73.004% | 275.669 GiB | 4.25 |
| MXFP8, group 32 | 5.959B | 0.781% | 5.723 GiB | 8.25 |
| BF16 | 3.419B | 0.448% | 6.369 GiB | 16.00 |
| FP32 | 0.042B | 0.006% | 0.158 GiB | 32.00 |
| Engram affine 4-bit, group 32 | 196.614B | 25.762% | 114.444 GiB | 5.00 |
| **Total** | **763.205B** | **100%** | **402.363 GiB** | **4.529** |

## Storage Breakdown

| Component | Logical weights | Storage |
|---|---|---|
| Language backbone | 551.881B | 300.132 GB / 279.520 GiB |
| Engram tables | 196.614B | 122.884 GB / 114.444 GiB |
| Vision encoder | 0.485B | 0.971 GB / 0.904 GiB |
| MTP head | 14.225B | 8.047 GB / 7.495 GiB |
| **Total** | **763.205B** | **432.033 GB / 402.363 GiB** |

## License

MIT, following the source model. See `LICENSE`.