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.
On this page
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.
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.
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.
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.
Kinds of models
- Chat and text generation: assistants, agents, coding, summaries.
- Embeddings: turn text into vectors for search and RAG. See 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 |
Safety rules we always apply
- Only
safetensorsand 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.
Still stuck?
Email support@bitsrun.com with what you tried and any request id you saw. A real person answers.