MODEL PROFILE

Sage-A1.5-Q6_K-GGUF

InternScience · Sage-A1.5-Q6_K-GGUF

> [!Note] > This repository contains **GGUF** model weights for Sage-A1.5, quantized to **Q6_K**, for deployment with llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes. > > For the original Hugging Face Transformers weights (compatible with Transformers, vLLM, SGLang, etc.), see InternScience/Sage-A1.5. **Sage‑A1.5** is a 35B Mixture‑of‑Experts agentic model based on **Qwen3.6** and **Agents-A1** from Shanghai AI Laboratory, built to advance long-horizon scientific problem solving through adaptive capability composition. Building upon **Agents‑A1**, **Sage‑A1.5** learns not only to solve individual tasks, but also to organize work and revise its approach during execution, highlighting three key capabilities: **Agentic Orchestration, Verification, and Open Scientific Discovery**. First, agentic orchestration extends single-agent execution through task delegation and parallel collaboration among subagents with isolated contexts, enabling complex tasks to be decomposed and addressed concurrently while reducing interference across concurrent subagents. Second, agentic verification strengthens long-horizon reliability through progress checking, evidence validation, and reflective strategy revision, allowing **Sage‑A1.5** to recognize unproductive search paths and pursue alternative approaches. Third, open scientific discovery extends agentic capabilities beyond answering predefined questions to conducting exploratory research, connecting scientific inquiry with interactive analysis and inspectable research artifacts. We hope **Sage‑A1.5** provides the community with an accessible foundation for open scientific discovery, offering a practical path from long-horizon task execution to collaborative, verifiable, and inspectable scientific exploration.

Text generationcodelicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/31 09:45:36
OVERVIEW

Model overview

> [!Note] > This repository contains **GGUF** model weights for Sage-A1.5, quantized to **Q6_K**, for deployment with llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes. > > For the original Hugging Face Transformers weights (compatible with Transformers, vLLM, SGLang, etc.), see InternScience/Sage-A1.5. **Sage‑A1.5** is a 35B Mixture‑of‑Experts agentic model based on **Qwen3.6** and **Agents-A1** from Shanghai AI Laboratory, built to advance long-horizon scientific problem solving through adaptive capability composition. Building upon **Agents‑A1**, **Sage‑A1.5** learns not only to solve individual tasks, but also to organize work and revise its approach during execution, highlighting three key capabilities: **Agentic Orchestration, Verification, and Open Scientific Discovery**. First, agentic orchestration extends single-agent execution through task delegation and parallel collaboration among subagents with isolated contexts, enabling complex tasks to be decomposed and addressed concurrently while reducing interference across concurrent subagents. Second, agentic verification strengthens long-horizon reliability through progress checking, evidence validation, and reflective strategy revision, allowing **Sage‑A1.5** to recognize unproductive search paths and pursue alternative approaches. Third, open scientific discovery extends agentic capabilities beyond answering predefined questions to conducting exploratory research, connecting scientific inquiry with interactive analysis and inspectable research artifacts. We hope **Sage‑A1.5** provides the community with an accessible foundation for open scientific discovery, offering a practical path from long-horizon task execution to collaborative, verifiable, and inspectable scientific exploration.

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

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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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。