MODEL PROFILE

Qwythos-27B-v1-GGUF

empero-ai · Qwythos-27B-v1-GGUF

GGUF quantizations of **empero-ai/Qwythos-27B-v1** for llama.cpp, Ollama, LM Studio, jan, KoboldCpp, and other GGUF runtimes. Qwythos-27B-v1 is a dense, hybrid Qwen3.5-27B—the larger continuation of Qwythos-9B, trained on the same curriculum at a larger scale. Nothing was removed to make this conversion fit: its native MTP head, vision tower, and 1,048,576-token (1M) context capability are intact. This is the pre-RL v1 checkpoint, following SFT, DPO, and ESFT; an RL-trained v2 will follow, while v1 is a complete checkpoint in its own right. For the full capability, training, and evaluation writeup, see the **base model card**. This card is intentionally focused on choosing a file and running it. The repository is named `Qwythos-27B-v1-GGUF`, but the files on disk deliberately use the shorter `Qwythos-27B-*` names below. Use those exact filenames when downloading or passing `-m`.

Visual QAreasoningmultimodallicense:apache-2.0
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/10/29 05:40:45
OVERVIEW

Model overview

GGUF quantizations of **empero-ai/Qwythos-27B-v1** for llama.cpp, Ollama, LM Studio, jan, KoboldCpp, and other GGUF runtimes. Qwythos-27B-v1 is a dense, hybrid Qwen3.5-27B—the larger continuation of Qwythos-9B, trained on the same curriculum at a larger scale. Nothing was removed to make this conversion fit: its native MTP head, vision tower, and 1,048,576-token (1M) context capability are intact. This is the pre-RL v1 checkpoint, following SFT, DPO, and ESFT; an RL-trained v2 will follow, while v1 is a complete checkpoint in its own right. For the full capability, training, and evaluation writeup, see the **base model card**. This card is intentionally focused on choosing a file and running it. The repository is named `Qwythos-27B-v1-GGUF`, but the files on disk deliberately use the shorter `Qwythos-27B-*` names below. Use those exact filenames when downloading or passing `-m`.

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:apache-2.0。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。