MODEL PROFILE · 模型档案

Qwen3.8-27B-GPTQ-W4A16

pearsonkyle · Qwen3.8-27B-GPTQ-W4A16

📦 18.1 GiB · 2.8× smaller compressed-tensors · int4 g128 sym 🧬 calibrated at ctx 32,768 ⚡ MTP bundled · 1.71× decode 👁️ Text + Image · vision 3/3 🏅 tool-calling = bf16, turn for turn 🏗️ vLLM 0.27.1 ⚡ What this is A compressed-tensors W4A16 (GPTQ, int4 group-128 symmetric) quantization of Qwen/Qwen3.8-27B that vLLM serves directly — no conversion, no custom runtime, quantization auto-detected from config.json . Calibrated on 4.26M tokens of real agentic-coding sessions packed at 32K context. Sibling of the GGUF ladder , same corpus: this one for vLLM , the GGUF for llama.cpp / Ollama / LM Studio. ⚡ Speculative decoding + vision, both bundled The trained MTP draft head ships inside the checkpoint — one --speculative-config flag, no second file, 1.71× decode at 71.2% acceptance . The vision tower ships too, kept at bf16: shown a synthetic test image it named the colour, shape and position of all three shapes correctly, unprompted detail included ("inverted triangle, base horizontal at the top"). Both are additive — drop the flags and you are back to the identical text model.

译文排队中,当前展示来源原文。

视觉问答multimodallicense:apache-2.0
上下文窗口来源未披露
输入 → 输出文本、图像 → 文本
模型权重待核验
档案更新时间2026/11/2 19:15:40
OVERVIEW

模型介绍

📦 18.1 GiB · 2.8× smaller compressed-tensors · int4 g128 sym 🧬 calibrated at ctx 32,768 ⚡ MTP bundled · 1.71× decode 👁️ Text + Image · vision 3/3 🏅 tool-calling = bf16, turn for turn 🏗️ vLLM 0.27.1 ⚡ What this is A compressed-tensors W4A16 (GPTQ, int4 group-128 symmetric) quantization of Qwen/Qwen3.8-27B that vLLM serves directly — no conversion, no custom runtime, quantization auto-detected from config.json . Calibrated on 4.26M tokens of real agentic-coding sessions packed at 32K context. Sibling of the GGUF ladder , same corpus: this one for vLLM , the GGUF for llama.cpp / Ollama / LM Studio. ⚡ Speculative decoding + vision, both bundled The trained MTP draft head ships inside the checkpoint — one --speculative-config flag, no second file, 1.71× decode at 71.2% acceptance . The vision tower ships too, kept at bf16: shown a synthetic test image it named the colour, shape and position of all three shapes correctly, unprompted detail included ("inverted triangle, base horizontal at the top"). Both are additive — drop the flags and you are back to the identical text model.

主要特点

特点摘要待依据来源补齐,不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

PRICING & COST

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价,不将渠道价格视为厂商官方统一价格。

来源 / 渠道输入输出计费单位采集时间
暂未取得可核验报价,请查阅来源渠道。

按月估算基础 Token 费用

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预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。

INDEPENDENT BENCHMARKS

公开评测与运行配置

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。

查看完整已收录榜单 →

常见问题

开放权重是否代表可以免费商用?

不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。

这些价格是实时结算价格吗?

不是实时结算价格,而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。

资料来源与可追溯记录

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。