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OsaurusAI/
DeepSeek-V4-Flash-0731-JANG

Name: DeepSeek-V4-Flash-0731-JANG
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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 is new in this release

Optimized weight codes. Every routed expert projection carries error-compensated (GPTQ-family) quantization codes fitted against real routed activation statistics, instead of plain nearest rounding. The storage format, scales, shapes, and kernels are completely unchanged — any loader that read the previous bundle reads this one.
Coding-tuned sampling default. temperature=0.6 is now the stamped deployment default (see below).
Live-verified. This exact artifact generates coherently in vMLX Python 0.22 with pool-cache quantization on; see the validation section.

Runtime requirement

Use vMLX Python 0.22 or newer (or an equivalent DSV4-aware runtime) 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.

This is not a uniform mlx_lm quant. A loader that applies one global bit width cannot interpret this bundle correctly.

Dynamic affine recipe

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 the actual per-tensor plan: 11,008 tensors at 2b/G32, 20,480 at 2b/G64, 1,536 at 3b/G64, and 512 at 8b/G64. AWQ FFN-input scales and diagonal down-input importance scales are folded into the weights, and the routed codes are additionally optimized for output reconstruction on the same affine grids; no runtime sidecar or custom kernel is required. This is affine JANG, not JANGTQ.

Native 0731 chat contract

The official release does not provide a Jinja chat template. This repository therefore does not synthesize chat_template.jinja and leaves tokenizer_config.json.chat_template 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"
)

Reasoning modes are:

Instruct: thinking_mode="chat";
Reasoning Low: thinking_mode="thinking", reasoning_effort="low";
Reasoning High: reasoning_effort="high";
Reasoning Max: reasoning_effort="max".

Tool calls use the native DSML grammar. Tool results are merged into user messages as <tool_result>...</tool_result> blocks. See encoding/README.md for the complete OpenAI-compatible message conversion and parser contract.

Generation and stop contract

The deployment generation_config.json contains:

text

do_sample=true
temperature=0.6
top_p=0.95
top_k=0
bos_token_id=0
eos_token_id=1

temperature=0.6 is this bundle's deliberate deployment default, tuned for coding and agentic use (it is also the setting DeepSeek used for DSV4 pass@1 coding evaluation). The upstream model card documents 1.0 as its general default; clients may explicitly select it, or any other policy, per request. The same defaults are declared in jang_config.json chat metadata so both declarations agree. There is no non-neutral repetition-penalty override.

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 and long-context contract

The bundle preserves the 1M-token YaRN configuration, sliding window 128, and the layerwise compression schedule. Its native cache metadata names SWA, CSA, HCA, Compressor, and Indexer 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. Enabling speculative decoding requires a separate drafter, draft cache, accept/reject verification, and atomic rollback of the full DSV4 composite cache; this bundle 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 OsaurusAI/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@osaurus.ai