---
library_name: llama.cpp
license: apache-2.0
pipeline_tag: image-text-to-text
base_model: InternScience/Agents-A1
tags:
- gguf
- llama-cpp
- multimodal
- vision
- moe
- agentic
---

# Agents-A1 — Q8_0 GGUF (with vision)

GGUF quantization of [`InternScience/Agents-A1`](https://huggingface.co/InternScience/Agents-A1) for [llama.cpp](https://github.com/ggml-org/llama.cpp), **including the vision projector** so the model keeps its multimodal (image + text) capability.

| File | Type | Size | Notes |
|------|------|------|-------|
| `agents-a1-q8_0.gguf` | text model, **Q8_0** | 36.9 GB | chat template + tokenizer embedded |
| `mmproj-Agents-A1-f16.gguf` | vision projector, **F16** | 0.9 GB | required for image input |
| `chat_template.jinja` | chat template | — | already baked into the GGUF; loose copy for `--chat-template-file` overrides |

Converted with `llama.cpp/convert_hf_to_gguf.py` (architecture `qwen35moe`, registered upstream as `Qwen3_5MoeForConditionalGeneration`). The chat template embedded in the GGUF is byte-identical to the upstream `chat_template.jinja` and includes the vision-token, tool-call, and `<think>` reasoning logic.

> [!IMPORTANT]
> Agents-A1 uses a **hybrid linear-attention + MoE** architecture (Qwen3.5-MoE family). You need a **recent llama.cpp build** that supports the `qwen35moe` inference graph — converter support landing does not guarantee your local binary can run it. If the model fails to load, update and rebuild llama.cpp from `master`.

## Usage

### Text + vision (recommended)

Download both GGUFs, then point `--mmproj` at the projector:

```bash
llama-server -m agents-a1-q8_0.gguf --mmproj mmproj-Agents-A1-f16.gguf -c 8192 -ngl 99
```

Then send images via the web UI or the OpenAI-compatible `image_url` message format.

### Text only

```bash
llama-cli -m agents-a1-q8_0.gguf -p "Hello"
```

Omitting `--mmproj` runs the language model alone and frees the compute/KV the vision encoder would use.

---

## About the original model

**Agents-A1** is a 35B Mixture-of-Experts agentic model from [InternScience](https://huggingface.co/InternScience), built to scale heterogeneous agentic abilities across **long-horizon search, engineering, scientific research, instruction following, and tool-calling**. Despite the ~35B class, it reports competitive-to-SOTA results against much larger frontier systems on benchmarks such as Seal-0, HiPhO, FrontierScience, IFBench, BrowseComp, and GAIA.

- 🏠 Homepage: https://internscience.github.io/Agents-A1/
- 📄 Technical Report: https://arxiv.org/abs/2606.30616
- 💻 GitHub: https://github.com/InternScience/Agents-A1

See the [original model card](https://huggingface.co/InternScience/Agents-A1) for full details, benchmarks, and licensing. All credit for the model goes to InternScience; this repo only provides GGUF conversions. License: Apache-2.0.