Volume-hosted models

Volume-hosted models are part of the Volumes beta.

Hosted SFT and RL runs can start from Hugging Face-format weights on a volume, including private models and weights exported from your own fine-tunes. Point [model].name at their absolute /volume/... path and attach the volume. No model-cache entry is needed. Use this for:

Weights that aren’t on Hugging Face, such as private or proprietary checkpoints.
Continuing your own fine-tunes from weights you exported to Hugging Face format.

Quick start

Create a volume and upload a complete Hugging Face model directory, including config.json, safetensors weights, and tokenizer assets:

prime volumes create research
prime volumes put research ./qwen3-hf models/

Point the config at them:

max_steps = 100

[ckpt]
interval = 50
keep_last = 2

[model]
name = "/volume/models/qwen3-hf"      # Qwen3 weights at this path

[data]
name = "datasets/my-dataset"          # the dataset can live on the same volume
type = "sft"
seq_len = 4096

[deployment]
num_train_gpus = 1

[renderer]
name = "prime-qwen3"                  # match the staged model's family

Launch with the volume:

prime train sft.toml --volume research

Size [deployment] for your model as described in Hosted SFT. This example uses Qwen3 weights; choose the renderer for your model family.

Use the model in an RL run

Start with a complete RL training config and replace its shared model name:

[model]
name = "/volume/models/qwen3-hf"

[orchestrator.renderer]
name = "prime-qwen3"

The shared name supplies the trainer, orchestrator, and inference model. If your config overrides model names for individual roles, change those names to the same directory too. Then launch with prime train config.toml --volume research. Set the renderer explicitly for the model family. A filesystem path cannot identify the family from a Hugging Face model ID; the Qwen3 example above uses prime-qwen3. RL configs with [trainer] and [orchestrator] blocks support volume model paths; a cached Hugging Face ID is not required for these paths.

Rules

Requirement	Rule
Volume required	The run must use the volume: `--volume <name>`, or `volume = "<name>"` in the config.
Path	Starts with `/volume/` and has at least one folder after it. Relative paths don’t work here, unlike `data.name`.
Path characters	Each part starts with a letter or digit, followed by letters, digits, `.`, `-`, or `_`. `.` and `..` aren’t allowed.
Weight format	A complete Hugging Face model directory, including config, weights, and tokenizer assets. A sharded training checkpoint is not a model directory: see below.
Model cache	Not used, and not checked.

The whole volume is mounted read-write, including earlier runs/ directories. The trainer writes weight conversions to scratch space rather than overwriting the source model.

Continue from a previous run’s checkpoint

Run checkpoints are in prime-rl’s sharded format, so they can’t be used directly as [model].name. Export a checkpoint to Hugging Face format, upload the exported directory under models/, and use its /volume/models/... path for a new SFT or RL run. Starting from exported weights begins a new run; it does not restore the previous optimizer or training step. To resume the full training state, set [resume].dir to the checkpoint’s step directory in the run TOML:

[resume]
dir = "/volume/runs/<runId>/checkpoints/step_<N>"

Getting Started

Inference

Model Factory

Community

Compute

Traces

Libraries

Quick start

Use the model in an RL run

Rules

Continue from a previous run’s checkpoint

See also

​Quick start

​Use the model in an RL run

​Rules

​Continue from a previous run’s checkpoint

​See also

Quick start

Use the model in an RL run

Rules

Continue from a previous run’s checkpoint

See also