---

license: cc-by-nc-4.0

base_model: FrontisAI/Frontis-MA1-35B

base_model_relation: quantized

library_name: llama.cpp

pipeline_tag: image-text-to-text

tags:

- openmle

- frontis-ma1

- gguf

- q4-k-m

- local-inference

- multimodal

- moe

- coding

---

Frontis-MA1-35B-GGUF

<p align="center">

<a href="https://arxiv.org/abs/2607.28568">📄 Paper</a>

&nbsp;•&nbsp;

<a href="https://frontisai.github.io/OpenRSI/">🌐 Project</a>

&nbsp;•&nbsp;

<a href="https://github.com/FrontisAI/OpenRSI">💻 Code</a>

&nbsp;•&nbsp;

<a href="https://huggingface.co/collections/FrontisAI/frontis-ma1">🤗 Models</a>

&nbsp;•&nbsp;

<a href="https://huggingface.co/datasets/FrontisAI/OpenMLE-Tasks">🧩 Tasks</a>

&nbsp;•&nbsp;

<a href="https://huggingface.co/datasets/FrontisAI/OpenMLE-SFT-Traces">📚 SFT Traces</a>

</p>

This repository is the official local-deployment derivative of Frontis-MA1-35B. It contains one Q4_K_M language-model file and the F16 multimodal projector required for image input with llama.cpp.

It accompanies the paper Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering and the OpenRSI code release.

Files

| File | Size | Purpose |

| --- | ---: | --- |

| Frontis-MA1-35B-Q4_K_M.gguf | 19.71 GiB | Q4_K_M language model |

| mmproj-Frontis-MA1-35B-F16.gguf | 857.62 MiB | F16 vision encoder/projector |

| checksums.txt | — | SHA-256 integrity manifest |

Only this deployment combination is published intentionally. The canonical BF16 Transformers weights remain in the base repository. This GGUF derivative does not publish a separate MTP draft-model variant.

Text and code quickstart

Tested conversion and inference tool: llama.cpp b9637, commit aedb2a5e9ca3d4064148bbb919e0ddc0c1b70ab3.

llama-cli \
  -m ./Frontis-MA1-35B-Q4_K_M.gguf \
  -ngl all \
  -c 32768 \
  -n 1024 \
  -cnv -st --simple-io \
  -p "Build a strong tabular classification baseline and explain the validation design."

Image quickstart

llama-cli \
  -m ./Frontis-MA1-35B-Q4_K_M.gguf \
  -mm ./mmproj-Frontis-MA1-35B-F16.gguf \
  --image ./example.jpg \
  -ngl all \
  -c 32768 \
  -n 512 \
  -cnv -st --simple-io \
  -p "Describe the image and identify information relevant to an ML workflow."

Reduce -c when memory is limited. On systems that cannot offload all layers, set -ngl to a smaller value or let llama.cpp choose automatically.

Release validation

Both final files passed SHA-256 verification and complete GGUF structure reads (733 language-model tensors and 334 projector tensors). The release also passed two real llama-cli smokes with full GPU offload on one NVIDIA H200: text generation from the Q4 file, and image-conditioned generation using the Q4 file with the F16 projector. These checks validate the release artifacts and command paths; they are not consumer-hardware speed benchmarks.

Component and evaluation scope

• The language-model weights are the OpenMLE post-trained Frontis-MA1-35B weights.
• The vision encoder/projector is inherited unchanged from Qwen3.6-35B-A3B and converted to F16 GGUF.
• OpenMLE post-training and the reported evaluations are text/code-only; they do not establish improved or fully validated visual capability.
• Q4_K_M is lossy. Use the BF16 repository when maximum fidelity or paper-result reproduction is required.
• The paper's reported scores measure the canonical model with the OpenMLE-Evo harness, not GGUF one-shot generation.

Generated code may be incorrect or unsafe. Execute it only in an isolated environment with explicit resource limits.

Paper result

The canonical BF16 model reaches 60.61% Medal Average and 0.7647 Human Rank with OpenMLE-Evo on the official 22-task MLE-Bench Lite split, compared with 39.39% and 0.5828 for its base model under the same harness. With OpenMLE-Evo-Max, the complete BF16 model–harness system reaches 71.21% and 0.8126. These are BF16 system results, not GGUF one-shot scores.

License

Original Frontis-MA1 material is released under CC BY-NC 4.0 for attribution-required, non-commercial use. Commercial use is not granted. The upstream Qwen Apache License 2.0 notice is preserved in LICENSE-UPSTREAM-APACHE-2.0 and NOTICE.