Frontis-MA1-35B-GGUF

📄 Paper  •  🌐 Project  •  💻 Code  •  🤗 Models  •  🧩 Tasks  •  📚 SFT Traces

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.