# Model names you can deploy

> Type an Ollama name like qwen3:8b, a Hugging Face repo or link, or one of your uploads, and BitsRun works out the format, GPU and runtime for you.

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

The **Model** box on the New endpoint form, `bitsrun deploy` and the mobile apps all accept the same names.

| You type | Example | Where it comes from |
|---|---|---|
| An Ollama name | `qwen3:8b` | The Ollama library |
| An Ollama name with an owner | `user/model:tag` | A community model on Ollama |
| A Hugging Face repo | `hf:Qwen/Qwen3-8B` | Hugging Face |
| A GGUF repo with a quant | `hf:unsloth/Qwen3-8B-GGUF:Q8_0` | Hugging Face, one chosen file |
| A Hugging Face link | `https://huggingface.co/Qwen/Qwen3-8B` | Paste it straight from your browser |
| A link to one .gguf file | `https://huggingface.co/owner/repo/blob/main/model-Q6_K.gguf` | Hugging Face, that exact file |
| Your upload | `upload:<id>` | A file you uploaded |

## Ollama names

The quickest option. Use the name and tag shown on the model's Ollama page: `qwen3:8b`, `gemma3:12b`, `llama3.1:8b`, `qwen2.5-coder:7b`. You can also write `ollama:qwen3:8b`. These are GGUF models and run on llama.cpp.

## Hugging Face repos

Write `hf:owner/repo`, or paste any huggingface.co link: the model page, a file page, a download link, or the "show file info" link. BitsRun accepts `hf://owner/repo` and `hf.co/owner/repo` too. The version in a link is ignored: every deploy uses the repo's latest version at that moment, then keeps it fixed.

Not sure what to copy from a model page? See [Copy the right name from Hugging Face](https://bitsrun.com/docs/models/hugging-face/).

What happens next depends on the files in the repo:

- **GGUF repo** (single-file quants): BitsRun picks one file. Without a quant it uses `Q4_K_M`, or the smallest file if there is none. Add `:Q8_0` (or any quant the repo lists) to choose another. Runs on llama.cpp.
- **Safetensors repo** (the original weights, as published with Hugging Face transformers): chat models run on vLLM, embedding models on Text Embeddings Inference.
- **Gated or private repo:** needs your Hugging Face token, and the files are copied into your private storage first. See [Gated and private repos](https://bitsrun.com/docs/models/gated-repos/).

## Your uploads

Upload a GGUF file or a safetensors folder, such as your own fine-tune, then deploy it as `upload:<id>`. See [Upload your own model](https://bitsrun.com/docs/models/uploads/).

## Formats in one line each

- **GGUF:** a single file that packs a model in a compressed form for llama.cpp. Good speed and low memory on one GPU.
- **Safetensors:** the standard, safe file format for model weights. Usually full precision, served by vLLM for high throughput.
- **Quant (quantization):** a smaller copy of a model with fewer bits per number, for example Q4_K_M. Smaller and faster, slightly lower quality. See [Quantization](https://bitsrun.com/docs/models/quantization/).

## Kinds of models

- **Chat and text generation:** assistants, agents, coding, summaries.
- **Embeddings:** turn text into vectors for search and RAG. See [Embeddings](https://bitsrun.com/docs/api/embeddings/).

Other kinds (image generation, speech, rerankers, vision) cannot be deployed yet. Vision-language repos are served as text only.

## Which model should I start with?

| You want | Try |
|---|---|
| A general assistant on the cheapest GPU | `qwen3:8b` |
| Coding help | `qwen2.5-coder:7b` |
| Search over your documents | `hf:Snowflake/snowflake-arctic-embed-l-v2.0` |
| Better answers, bigger budget | A 14B to 32B model on L40S, see the [model sizer](https://bitsrun.com/docs/models/model-sizer/) |

## Safety rules we always apply

- Only `safetensors` and GGUF files. Older formats that can run code when loaded (pickle files) are never used.
- Models that need their own Python code (`trust_remote_code`) are refused. Pick a version without it, or a GGUF version.