DeepSeek-V4-Flash-0731 — imatrix-calibrated mixed 2/3/4/8-bit for mlx-serve

Please use https://huggingface.co/ddalcu/DeepSeek-V4-Flash-0731-iQ-MLX-3.3bpw Instead of this model

A 115.4 GB mixed-precision MLX conversion of deepseek-ai/DeepSeek-V4-Flash-0731, built to run the full 284B-A13B model on a single Apple Silicon Mac with 128 GB or more of unified memory.

Runs on mlx-serve — a native Zig inference server for MLX models on Apple Silicon, with no Python in the serving path. It speaks the OpenAI, Anthropic and Ollama HTTP APIs, so existing clients (Claude Code, pi, opencode, Open WebUI, …) point at it unchanged.

mlx-serve --model <this-repo-dir> --serve --port 11434

DeepSeek-V4-Flash's architecture is implemented natively in mlx-serve: MQA over a single 512-dim latent, window-128 raw attention plus gated-pooling compressed history with a top-512 indexer, per-head attention sinks, Sinkhorn hyper- connections, hash-routed early MoE layers, and the DSML tool-call format. No llama.cpp, no GGUF conversion, no Python runtime.

How it was quantized

Every tensor class is sized by what it costs and how much it matters, rather than one global bit width:

The routed experts — 277B of the 284B — are quantized with an activation-calibrated search rather than plain min/max: per-input-channel importance comes from an importance matrix collected over 1.5M tokens of chat-formatted text (antirez's DeepSeek-V4-Flash imatrix from the ds4 gguf-tools, per-expert channel granularity), and each quantization group's scale/bias pair is chosen by a weighted multi-start search with alternating least-squares refinement (the llama.cpp make_qkx2_quants pattern). Channels that actually fire reconstruct better; at 2-3 bits this is worth more than finer group granularity, which is why the experts use group size 128 and spend the saved bytes nowhere — the model just gets smaller and faster.

The last four layers keep their experts at 4-bit. This is not a nicety, it is what makes the model usable as a coding agent: with 2-bit experts all the way to the top, the final layers sit on near-tie logit boundaries and the model falls into turn-level repetition loops in agent sessions, re-issuing the same tool call dozens of times instead of concluding. Raising just those four layers fixes it (details in the revision history below).

Two more choices worth explaining. The down-projection keeps 3-bit while gate/up drop to 2-bit: it is the most quantization-sensitive of the three. The DSpark draft stages keep 4-bit, uncalibrated (the imatrix does not cover them): they are a rounding error on disk, and a draft the trunk rejects costs a full verify forward, so their quality multiplies throughput.

The compressor path is fp32-sensitive by design and the router is read raw, so neither is quantized. Lookup tables (embeddings, the token→expert hash, DSpark's Markov table) are never packed — they are gathered, not multiplied.

Conversion is exact where it can be: the source's fp8 (e4m3 + e8m0 block scales) and fp4 (e2m1 + e8m0 group scales) formats all fit losslessly in bf16, so the weights are decoded exactly before requantization. The calibrated expert packs are byte-compatible with MLX's affine layout, so the mirror is engine-native — no dequantize-on-load step at runtime.

What is included

Weights, tokenizer, and a chat template transcribed from the release's own encoding/encoding_dsv4.py and verified byte-exact against it across chat and thinking modes, tool definitions, DSML tool-call history, multi-turn drop-thinking, and all three reasoning-effort levels. generation_config.json carries the reference's own default sampling (temperature 0.6), not the wild 1.0/1.0 signature the source ships.

DSpark speculative-decoding weights (3 draft stages) are included and converted; mlx-serve drives them with --dspark (block-parallel speculative decode, greedy and sampled).

Requirements

• Apple Silicon Mac, 128 GB+ unified memory (~98 GB resident, +11 GB with --dspark)
• macOS 26.2 or newer
• mlx-serve

Built with tests/convert_dsv4_weights.py from the mlx-serve repo.

Revision history

• 2026-08-02 (tail fix): routed experts on the last 4 layers (39-42) raised from 2/3-bit to 4-bit, group size 64. The uniform low-bit build had a behavior bug in agent use: it would finish the work, tests green, then loop on the same verification tool call until the client gave up. In a repeated A/B on an identical coding-agent task the uniform build looped in 3 of 4 runs and spent 16-28K tokens per task; this build looped in 0 of 3 and spent 5-8K tokens, with identical correctness on the task's 50-vector check and identical decode speed (~53 tok/s with --dspark on an M-series 128 GB). 115.4 GB instead of 109.2. Every shard below layer 39 is byte-identical to the previous revision.
• 2026-08-02: routed experts requantized with a fresh imatrix collected on the 0731 weights themselves (2.9M tokens of the chat-v2 corpus through antirez's official 0731 GGUF, 747M routed-expert observations). The previous revision was calibrated with the only published imatrix at the time, which turned out to be collected on the preview checkpoint; the 0731 retrain moved the per-expert activation statistics substantially (median correlation 0.66 against preview), mostly on the down projections. Better verbatim precision in code and text. Spine, embeddings and DSpark draft stages are byte-identical to the previous revision.
• 2026-08-01: routed experts rebuilt with imatrix-calibrated quantization at group size 128 (previously uncalibrated min/max at group size 64). Better character-level fidelity in verbatim text and code edits, 109.2 GB instead of 117.8, and more usable context headroom on 128 GB Macs.
• Earlier revisions remain available through this repo's git history.