MODEL PROFILE

DeepSeek-V4-Flash-0731-REAP-150b-TQ3_4S-GGUF

Tinker-Stack · DeepSeek-V4-Flash-0731-REAP-150b-TQ3_4S-GGUF

A **custom, selectively-quantized GGUF** of the DeepSeek-V4-Flash-0731 architecture, pruned to the 150B "REAP" variant, quantized with a hand-tuned per-tensor recipe and wrapped in the **TurboQuant `TQ3_4S` container** from the `turbo-tan/llama.cpp-tq3` fork. This is **not** a plain uniform quant. It is the result of iterative quantization on a 3× Turing GPU box, and the recipe reflects a specific, measured trade-off: maximize context length (256K) while holding wikitext-2 perplexity within noise of the IQ3_XXS baseline that proved to be the local optimum for this model. > [!WARNING] > **Experimental build.** This is a hand-tuned, selectively-quantized experimental release, > not a polished upstream quant. It requires the non-stock > `turbo-tan/llama.cpp-tq3` fork to load > (`TQ3_4S` container is rejected by stock llama.cpp), the recipe and serving flags are > tuned for our specific 3× Turing hardware (§3, §6), and the benchmarks in §4 are our own > internal measurements, not standardized leaderboard runs. Expect rough edges, and treat > the numbers as relative to sibling builds on the same rig rather than absolute claims. > If you just want a drop-in quant of this model, use the upstream REAP release instead.

Text generationreasoninglicense:mit
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/31 03:05:47
OVERVIEW

Model overview

A **custom, selectively-quantized GGUF** of the DeepSeek-V4-Flash-0731 architecture, pruned to the 150B "REAP" variant, quantized with a hand-tuned per-tensor recipe and wrapped in the **TurboQuant `TQ3_4S` container** from the `turbo-tan/llama.cpp-tq3` fork. This is **not** a plain uniform quant. It is the result of iterative quantization on a 3× Turing GPU box, and the recipe reflects a specific, measured trade-off: maximize context length (256K) while holding wikitext-2 perplexity within noise of the IQ3_XXS baseline that proved to be the local optimum for this model. > [!WARNING] > **Experimental build.** This is a hand-tuned, selectively-quantized experimental release, > not a polished upstream quant. It requires the non-stock > `turbo-tan/llama.cpp-tq3` fork to load > (`TQ3_4S` container is rejected by stock llama.cpp), the recipe and serving flags are > tuned for our specific 3× Turing hardware (§3, §6), and the benchmarks in §4 are our own > internal measurements, not standardized leaderboard runs. Expect rough edges, and treat > the numbers as relative to sibling builds on the same rig rather than absolute claims. > If you just want a drop-in quant of this model, use the upstream REAP release instead.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Source / channelInputOutputBilling unitCollected at
No verified prices yet. Check the source channel.

Estimate monthly base token costs

—

Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.

INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

View full indexed rankings →

Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

Are these real-time billing prices?

These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.

Sources & traceability

许可证:mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。