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The Base Model picker in Fine-tune a Model can search HuggingFace directly, but this page is about the other path: uploading your own weights as a .zip, the same flow whether you start it from Eval’s Models page or from Train. It’s the same Add Model screen either way, so the steps below walk it end to end, focused on the File tab rather than the HuggingFace search tab.

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Step 1: Open Models and click Add Model

In the left sidebar, click Models, then + Add Model in the top right.
The AI Models page listing deployed and base models, with the Add Model button in the top right

The AI Models page, with Add Model in the top right

This is the same Models list Eval uses, every model you register here, uploaded or imported, is available to both Eval benchmarks and Train fine-tuning jobs. You don’t register a model twice for the two sections.

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Step 2: Name the model

Give it a name and, optionally, a short description, then click Add Model to continue.
The Add Model panel with a model name field, a description textarea, and Cancel/Add Model buttons

The Add Model panel: name and description, before you've picked a source

The model is created as a Draft at this point, nothing is uploaded yet. Naming happens before you pick a source because the same draft can be filled in either way, by file or by HuggingFace, on the next screen.

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Step 3: Upload the archive on the File tab

The model card opens on an Add Model card with two tabs, File and HF model. Stay on File: drag and drop your .zip onto the dashed area, or click Browse Files.
The File tab on the Add Model card, showing a dashed drag-and-drop zone reading 'A .zip with config.json and model weights', a Browse Files button, and a disabled Add model button

The File tab: drag-and-drop area for a model archive, with Browse Files as the alternative

The Add model button underneath stays disabled until a valid file is attached, BenchGen validates the archive in your browser before it starts uploading, so a bad archive fails immediately instead of after a multi-GB upload. This is the same File tab documented in Add a Model → Option A — Upload a file, whether you get there from Eval’s Models page or from here.

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What has to be inside the zip

A Windows archive manager showing ft-Qwen3-0.6B-mq102kd2_merged.zip containing config.json, model.safetensors, tokenizer.json, tokenizer_config.json, generation_config.json, and chat_template.jinja

A merged model's zip contents, viewed on Windows: config.json, model.safetensors, and the tokenizer files, no subfolder

No executables or scripts, inside the zip or in its filename. Archives containing .exe, .sh, .bat, .py, and similar are rejected outright, and so is an archive whose own filename looks like one of those.

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Which HuggingFace models you can and can’t use

GGUF models cannot be fine-tuned. GGUF is an inference-only, quantized format for llama.cpp / Ollama, there’s no full-precision weight tensor for a LoRA adapter to attach to. If a repo’s name ends in -GGUF, that’s the tell.
If you want a smaller memory footprint during training, use the training job’s own Quantization option (QLoRA-style), not a pre-quantized checkpoint downloaded from the Hub.

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A short model catalog to start from

Open-weight, instruction-tuned models that are known to work well as fine-tuning bases:
Prefer the smallest model that’s plausible for your task. A 1.5B–8B instruction-tuned model with QLoRA trains far faster and cheaper than reaching for the largest checkpoint you can find.

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Step 4: Confirm the model is ready

Once the upload finishes, BenchGen registers the model and provisions its endpoint. The status flips from Draft to ready, and the model card fills in its model name, Endpoint URL, access Token, and format (for example SAFETENSORS).
A model card showing Deployed and ready status, its model name, endpoint URL, token, upload date, and SAFETENSORS format

A registered model, ready, showing its endpoint details and format

It now appears under My Models in the Base Model picker on the Fine-tune a Model form, ready to select as your starting point.

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Next Steps

Fine-tune a Model

Pick this model as your base and configure the run.

Add a Model

The same Add Model flow, covering the HuggingFace import path too.
Last modified on September 8, 2026