You added Modal + HF credentials. What do you want me to do with them? We want to leverage the modal advantages with lora and finetuning within our 251 dollar hackathon credit to making special something to our space, space is working on HF zero gpu blackwell nvidia system 100 gb vram we do not need vram from modal directly and also hackathon rule is limiting the 32B in total modal context so make deep search and deep think for making this space better, and try to embed the space here too for better easy edit and controls " This Space is currently using 0 RTX Pro 6000 Blackwell. CPU 0% RAM 1/104 GB " " curl https://huggingface.co/spaces/build-small-hackathon/NEXUS_Visual_Weaver/agents.md " " Access Space logs via API Use an access token for authorization # Get container logs (SSE) curl -N \ -H "Authorization: Bearer $HF_TOKEN" \ "https://huggingface.co/api/spaces/build-small-hackathon/NEXUS_Visual_Weaver/logs/run" # Get build logs (SSE) curl -N \ -H "Authorization: Bearer $HF_TOKEN" \ "https://huggingface.co/api/spaces/build-small-hackathon/NEXUS_Visual_Weaver/logs/build" " Clone this Space repository # Make sure git-xet is installed (https://hf.co/docs/hub/git-xet) winget install git-xet git clone https://huggingface.co/spaces/build-small-hackathon/NEXUS_Visual_Weaver # If you want to clone without large files - just their pointers GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/spaces/build-small-hackathon/NEXUS_Visual_Weaver # Make sure the hf CLI is installed powershell -ExecutionPolicy ByPass -c "irm https://hf.co/cli/install.ps1 | iex" # Download the Space hf download build-small-hackathon/NEXUS_Visual_Weaver --repo-type=space " " This Space is best run on a GPU, so please ensure you have nvidia-docker2 installed. Installation guide docker run -it -p 7860:7860 --platform=linux/amd64 --gpus all \ -e HF_TOKEN="YOUR_VALUE_HERE" \ registry.hf.space/build-small-hackathon-nexus-visual-weaver-a107340:latest python app.py Available versions latest You need to switch on the new containerd engine in Docker for pulling and storing images. This will be the new Docker default in the future. Read more This Space requires some secrets to run, don't forget to set the environment values in your docker command! Quick Links How to run any Space as a Docker image Docs on the Docker Hub (docker.com) " " https://modal.com/docs/guide " " https://modal.com/docs/examples/modal-vibe "
This is a substantial, research-heavy request. Let me do the deep research you asked for — fetching Modal's docs, the Space's agents.md, and reviewing the current backend architecture in parallel.
The agents.md returned the README (no separate agents.md). Now let me read the actual backend runtime files to understand the integration seams.
Let me examine the LoRA adapter module and how generation is wired into the app and relay before planning.
Let me check how the parameter budget / 32B ceiling is tracked, since the hackathon rule is central here.
I have a solid understanding now. Let me locate where the 32B parameter budget is tracked across the project.
I've now mapped the architecture thoroughly. Here's what I found and where Modal fits:
Current state:
hf_runtime.generate_flux_image() — FLUX.2 Klein 9B/4B, gated by hf_runtime_enabled().lora_adapter.load_and_apply() applies pre-existing adapters from ADAPTER_CATALOG at inference time. There is no training/fine-tuning anywhere.provider_runtime.py) are governed HTTP adapters returning structured ...JudgeResult dataclasses with missing_secret/success/failed states.NEXUS_DISABLE_REAL_HF=1).The opportunity: The Blackwell Space is great for inference but the one thing it doesn't do is train custom LoRAs. That's exactly what Modal's serverless GPUs + your $251 credit are ideal for — and it keeps you well under the 32B Modal-context ceiling (FLUX.2 Klein 9B fine-tuning ≪ 32B). The differentiator: a governed LoRA fine-tuning lane — references in → trained couture adapter pushed to HF Hub → loaded back into the Blackwell Space for inference.
This is a large, multi-file architectural change with real decisions, so let me plan it properly before writing code.
Before I finalize the plan, a few decisions will shape the implementation. Let me confirm these.
Which Modal GPU should the LoRA fine-tuning lane target? B200 (Blackwell) (Recommended)
How should training data reach the Modal job? HF Dataset repo id (Recommended)
Where should trained LoRA adapters be published so the Blackwell Space can load them? Push to HF Hub (Recommended)
Besides the Modal training lane, do you also want the live HF Space embedded in the app UI? Yes, add an embedded preview (Recommended)
All four recommended directions confirmed. Let me read the key files in full to ground the plan in the actual code structure.
Now let me read the LoRA adapter, schema, catalog, and the app.py generation/UI wiring sections.
Now let me examine the app.py UI wiring around generation and the command center, plus how the existing tests are structured.
I now have a complete picture of the architecture. Let me write the plan.
I approve the plan, let's build it.