Sage-A1.5 GGUF (Q4_K_M)

Sage-A1.5 GGUF (Q4_K_M)

Runs Sage-A1.5, a 35B mixture-of-experts agentic model for long scientific tasks, as a 4-bit GGUF file. It can split work across subagents and check its own progress, for llama.cpp, Ollama and LM Studio.

Made by InternScience on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

About

What it is: This is a Q4_K_M GGUF copy of Sage-A1.5, a 35B mixture-of-experts agentic model from Shanghai AI Laboratory's InternScience group. It is built on Qwen3.6 and Agents-A1 for long scientific problem solving. At 4-bit the file is about 17.8 GB, and it loads in LM Studio, Ollama, llama.cpp or any similar GGUF tool.

Who it suits: Researchers and developers who want a local agentic model for multi-step analysis, tool use and exploratory science.

Industry | Who uses it | How they use it | Example
Scientific research | Research teams | Break a long problem into steps and check evidence along the way | A literature analysis split among subagents
Software | Agent developers | Build tool-using agents on a local model | An agent that calls a code interpreter
Education | Students and teachers | Study how an agent plans and revises its approach | A walkthrough of a multi-step analysis
Data analysis | Analysts | Run exploratory analysis with inspectable results | A notebook-style investigation

If you only need quick chat answers, you can skip it, since it is aimed at long, multi-step tasks.

Where to use it:

• llama.cpp, Ollama and LM Studio
• Other GGUF-compatible programs on Windows, macOS or Linux
• Agent setups that call tools through function calling

Use case | Who | What they get back
Agentic orchestration | Agent developers | Parallel investigations by subagents with separate contexts
Verification mode | Researchers | A self-review step inside the model's own reasoning
Tool use | Developers | Function calls to APIs, code interpreters and search

How to set it up:

• Python, to run the Hugging Face download tool
• About 17.8 GB of disk space for the Q4_K_M file
• A program that loads GGUF files, with enough memory for a 35B model

1. Install the Hugging Face hub tools with pip.
2. Download the GGUF file with the hf command.
3. Load it in llama.cpp, Ollama, LM Studio or another GGUF program.

pip install -U huggingface_hub
hf download InternScience/Sage-A1.5-Q4_K_M-GGUF

How to use it: Load the file in your GGUF program and give it a multi-step task, along with any tools it may call. Verification mode is opt-in, and the card says the model pauses mid-task to review what worked and what failed.

Example: You give the model a research question and a code interpreter tool. It breaks the question into steps, can hand parts to subagents, checks its evidence and returns an analysis you can inspect.

For newcomers and experts: Start with a short task that needs two or three steps and one tool. Add verification mode afterward and compare how the model handles a dead end.

The card describes three capabilities: orchestration through parallel subagents with isolated contexts, verification through progress and evidence checks, and open-ended scientific exploration. The model reviews itself during its own reasoning, so no separate checker is involved. The base weights for Transformers, vLLM and SGLang are in a separate repository.

Give a multi-step task and tools
Model plans and may split work
Subagents investigate in parallel
Model verifies evidence
A checked result comes back

Good to know:

• License: Apache 2.0
• Cost to run: none named for the local file, and the card mentions a free trial of a hosted service
• Data it reaches: downloads files from Hugging Face, and any tools you connect decide what else it can reach
• Not for: Transformers, vLLM or SGLang, which use the separate original weights

On Hugging Face, 13 November 2026

• Task: text generation
• Library: gguf
• Parameters: 35.5B
• Weights: about 17.8 GB at 4-bit (estimate)
• Formats: GGUF
• Downloads in the last 30 days: 636,834
• Likes: 84
• License: apache-2.0
• Based on: InternScience/Sage-A1.5
• Fine-tunes: 1
• Tags: gguf, vision, agentic, code, tool-use, text-generation, conversational

Links

• Model: https://huggingface.co/InternScience/Sage-A1.5-Q4_K_M-GGUF
• Files: https://huggingface.co/InternScience/Sage-A1.5-Q4_K_M-GGUF/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download InternScience/Sage-A1.5-Q4_K_M-GGUF

Then follow the usage in its model card. Nothing in this kit runs by itself.