# Copy the right name from Hugging Face

> Paste the model page link into the Model box; when the files sit in folders, paste the link to one .gguf file, and read the file list first to spot split or oversized models.

Source: https://bitsrun.com/docs/models/hugging-face/
Updated: 2026-11-09

You found a model on Hugging Face and want to run it. This page shows what to copy, where to copy it from, and how to tell in a minute whether the model can be deployed.

## The short answer

Copy the address of the model page from your browser and paste it into the **Model** box:

```
https://huggingface.co/unsloth/Qwen3-8B-GGUF
```

Then select **Check model** (or run `bitsrun size <link>`). BitsRun reads the file list, picks the file and the GPU, and tells you the price before you pay anything.

One thing to avoid: do not type `owner/repo` on its own. A name in that shape is read as an Ollama community model. Put `hf:` in front (`hf:unsloth/Qwen3-8B-GGUF`) or paste the full link.

## Where to copy from

| On the Hugging Face page | What you get | Paste it as |
|---|---|---|
| The address bar | `https://huggingface.co/owner/repo` | As it is |
| The copy icon next to the model name | `owner/repo` | `hf:owner/repo` |
| **Use this model**, then **Ollama** | `ollama run hf.co/owner/repo:Q4_K_M` | Only the `hf.co/owner/repo:Q4_K_M` part |
| **Files and versions**, then one `.gguf` file | A link with `/blob/main/` in it | As it is |

## Read the file list first

Open **Files and versions** on the model page. What you see there decides what to paste.

| You see | What to do |
|---|---|
| `.gguf` files at the top level, each named after its quant (`...-Q4_K_M.gguf`, `...-Q8_0.gguf`) | Paste the page link. BitsRun uses `Q4_K_M`. For another one, add it: `hf:owner/repo:Q8_0` |
| `.gguf` files inside folders | Open the file you want and paste that file's link. The page link alone gives `model_choose_file` |
| File names that end in `-00001-of-00006.gguf` | The model is split into parts and cannot be deployed yet. Look for a repo with single-file quants |
| `.safetensors` files with a `config.json` | Paste the page link. Chat models run on vLLM, embedding models on Text Embeddings Inference |
| Only `.bin` or `.pt` files | These are never used. Look for a safetensors or GGUF version |

## Check the size

The size shown next to each file tells you which GPU it needs. The model must fit in GPU memory with room left for the conversation, so the limits are lower than the memory on the card:

| File size | GPU |
|---|---|
| Up to about 18 GB | One NVIDIA L4 (24 GB) |
| Up to about 38 GB | One NVIDIA L40S (48 GB) |
| Up to about 78 GB | 4x NVIDIA L4 (96 GB) |
| Larger | Does not fit. Choose a smaller quant or a smaller model |

Very large models, hundreds of gigabytes each, are usually published as one folder per quant with every quant split into parts. Those cannot be deployed: the files are split, and even the smallest quant is larger than the biggest GPU.

Check model gives the exact answer. These numbers only help you skip a model that has no chance. See [Model sizer and GPU sizes](https://bitsrun.com/docs/models/model-sizer/).

## If Check model says no

| Message | Meaning | Next step |
|---|---|---|
| `model_choose_file` | The files are in folders | Paste the link to one `.gguf` file |
| `model_split_unsupported` | The files are split into parts | Pick a repo with single-file quants |
| `model_quant_not_found` | The repo has no file with that quant | Use a quant from the file list |
| `model_too_large` | Larger than every GPU | Pick a smaller quant |
| `model_gated` | The repo asks you to accept its terms | Add your [Hugging Face token](https://bitsrun.com/docs/models/gated-repos/) |

Every reason is explained in [Models that cannot be deployed](https://bitsrun.com/docs/models/unsupported/).