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JANGQ-AI/
DeepSeek-V4-Flash-0731-JANG

Name: DeepSeek-V4-Flash-0731-JANG
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[!IMPORTANT] This bundle requires vMLX Python 0.22 or newer. Earlier runtimes do not implement the DSV4 composite-cache and pool-quantization contract this bundle is stamped for.

JANG

⚡ All JANG models are meant to be run in vMLX

DeepSeek-V4-Flash-0731-JANG

Dynamic affine JANG quantization of the official deepseek-ai/DeepSeek-V4-Flash-0731 release for DSV4-aware Apple Silicon MLX runtimes, with QAT-grade error-compensated weight codes on all routed experts.

This is the 0731 release, not the earlier DeepSeek-V4-Flash preview. The source is pinned to immutable commit 9e165c30e2704aec5d9d593cce3eebd58bbef1cb.


Source	`deepseek-ai/DeepSeek-V4-Flash-0731`
Source revision	`9e165c30e2704aec5d9d593cce3eebd58bbef1cb`
License	MIT, inherited from upstream
Format	JANG mixed affine, GPTQ-optimized codes
Bundle size	102.00 GB / 94.995 GiB
Weight shards	102
Indexed tensor keys	101,295
Context configuration	1,048,576 tokens with YaRN
Runtime cache schema	`deepseek_v4_v9`
Measured decode	~20 tok/s steady on a 128 GB M5 Max, stock OS config
MTP / DSpark	Dropped from this runtime artifact

What this quant is

The 94.995 GiB policy protects the non-routed attention, Compressor, Indexer, shared-expert, embedding, and output paths at 8-bit. Routed down projections use group 32, gate/up use group 64, and the gate projection is lifted to 3-bit in six selected layers. Folded AWQ and diagonal-imatrix scales improve the weight fit, and every routed projection additionally carries error-compensated (GPTQ-family) codes fitted against real routed activation statistics instead of plain nearest rounding — same grids, same storage format, same kernels, no runtime sidecar.

Tensor role	Bits	Group size	Policy
Routed expert gate / `w1`	2	64	Default
Routed expert gate / `w1` in layers 5, 14, 30, 34, 37, 42	3	64	Quality lifts
Routed expert down / `w2`	2	32	All routed layers
Routed expert up / `w3`	2	64	All routed layers
Attention, Compressor, Indexer, shared expert	8	64	Non-routed fidelity floor
Token embedding and output head	8	64	Bookends
Norms, router, mHC, sinks and controls	Source dtype	—	Critical F32 retained

The index records 11,008 tensors at 2b/G32, 20,480 at 2b/G64, 1,536 at 3b/G64, and 512 at 8b/G64. This is affine JANG, not JANGTQ.

JANG versus uniform MLX quantization

	This repository	Uniform MLX quant
Weight policy	Per tensor role, projection, and selected layer	One global/default policy
Routed gate/down/up groups	G64 / G32 / G64	Not independently represented
Sensitive gate lifts	Six layers at 3-bit	Not represented by a global bit width
Weight codes	Error-compensated against routed activations	Nearest rounding
DSV4 controls	Source dtype; critical F32 retained	Loader-dependent
Required runtime	DSV4-aware vMLX mixed-affine path	Stock uniform loader

No like-for-like accuracy benchmark against a uniform MLX artifact is claimed. A stock loader that applies one bit width to every quantized tensor cannot faithfully interpret this bundle's plan.

Runtime requirement

Use vMLX Python 0.22 or newer, supporting:

per-tensor JANG affine bits and group sizes;
DSV4 SWA + CSA + HCA composite cache state;
Compressor and Sparse Indexer state;
the bundled official 0731 Python encoder and DSML output parser;
native low, high, and max reasoning effort, with Low as the reasoning default.

Preview-era adapters that expose only High/Max or map omitted reasoning to Instruct are incompatible with the 0731 contract.

Native 0731 chat contract

The official release has no Jinja chat template. This repository does not synthesize chat_template.jinja; tokenizer_config.json.chat_template is unset. Use encoding/encoding_dsv4.py:

python

from encoding.encoding_dsv4 import (
    encode_messages,
    parse_message_from_completion_text,
)

messages = [{"role": "user", "content": "Explain why 17 is prime."}]

# Native default reasoning: thinking mode, Low effort.
prompt = encode_messages(messages, thinking_mode="thinking")

# Non-reasoning / Instruct behavior.
chat_prompt = encode_messages(messages, thinking_mode="chat")

# Explicit 0731 reasoning rails.
high_prompt = encode_messages(
    messages, thinking_mode="thinking", reasoning_effort="high"
)
max_prompt = encode_messages(
    messages, thinking_mode="thinking", reasoning_effort="max"
)

The four intended surfaces are Instruct, Reasoning Low, Reasoning High, and Reasoning Max. Low is the default when reasoning_effort is omitted in thinking mode. Tool calls use native DSML, and tool results are merged into user messages as <tool_result>...</tool_result> blocks. See encoding/README.md for the full contract.

Generation and stop contract

The deployment generation_config.json uses do_sample=true, temperature=0.6, top_p=0.95, top_k=0, BOS 0, and EOS 1, with no non-neutral repetition-penalty override.

temperature=0.6 is this bundle's deliberate deployment default, tuned for coding and agentic use (also DeepSeek's DSV4 pass@1 coding-eval setting). The upstream card documents 1.0 as its general default; clients may explicitly select any policy per request. The same defaults are declared in jang_config.json chat metadata so both declarations agree.

DSV4-aware servers should recognize EOS 1 and the 0731 role-boundary tokens User 128803, Assistant 128804, and latest-reminder 128828 where the API surface uses boundary stopping.

Cache, context, and speculative decoding

The bundle preserves the 1M-token YaRN configuration, sliding window 128, and the layerwise compression schedule. Its deepseek_v4_v9 metadata names SWA, CSA, HCA, Compressor, and Indexer cache state. Generic TurboQuant KV is disabled and native q8 pool-cache quantization defaults on. Set DSV4_POOL_QUANT=0 only for an explicit diagnostic comparison.

MTP / DSpark weights are intentionally absent from this runtime artifact. Speculative decoding would require a separate drafter plus atomic rollback of the complete DSV4 composite cache; this repository does not claim that path.

Validation

Verified on this exact artifact:

source identity, the complete 102-shard index, all 101,295 safetensor header keys, the per-tensor affine plan, tokenizer metadata, generation defaults, and all four official encoder fixtures;
live generation in vMLX Python 0.22 with pool-cache quantization on: exact-instruction following, reasoning-Low arithmetic, a 400-token code generation row with exact requested identifiers, and a tool-call row — all coherent, naturally stopped, with no degenerate repetition;
~20 tok/s steady decode, ~7 s load, ~98 GB peak unified memory on a 128 GB M5 Max with stock OS configuration.

Not exhaustively re-verified on this exact artifact: the full multi-turn DSML tool matrix, 30K+ long-context recall, and cache trim/restart rows. No claim beyond the verified rows is made.

Download

bash

hf download JANGQ-AI/DeepSeek-V4-Flash-0731-JANG \
  --local-dir ~/models/DeepSeek-V4-Flash-0731-JANG

Korean summary

이 모델은 공식 deepseek-ai/DeepSeek-V4-Flash-0731 릴리스를 Apple Silicon 용으로 AWQ 및 diagonal imatrix가 적용된 affine JANG 양자화에 GPTQ 계열 오류-보상 코드 최적화를 더한 94.995 GiB 번들입니다. 저장 포맷과 커널은 기존과 동일하며, 배포 기본 샘플링은 코딩에 맞춘 temperature 0.6 / top-p 0.95입니다. 기본 추론 모드는 Reasoning Low이고 Low/High/Max와 비추론 Instruct 모드를 지원합니다. vMLX Python 0.22에서 이 번들 그대로 일관된 생성(정확한 지시 수행, 코드 식별자 재현, 도구 호출)과 128 GB M5 Max 기준 약 20 tok/s 디코드를 확인했습니다. 장문 컨텍스트 전체 매트릭스는 아직 완전히 재검증되지 않았습니다.

Contact

eric@jangq.ai