LoRA adapters

LoRA adapters let you customize a supported base model with your own PEFT-trained weights. You upload the adapter, register it against one or more eligible base models, and Valar validates it before it becomes usable.

LoRA is rolling out in phases. Today you can upload, register, and manage adapters, and Valar validates them per base model. Running a registered adapter at request time is coming soon, and this page will be updated with the request syntax when it ships.

Entitlement

LoRA is enabled per organization. If your org is not entitled, the LoRA and file-upload endpoints return 403 with lora entitlement required. Contact your Valar representative to turn it on.

Supported base models

An adapter can only target a base model that Valar has marked LoRA-eligible. Each eligible model declares a maximum adapter rank and the attention and MLP modules a LoRA may target.

Base model	Max rank	Target modules
`zai-org/GLM-5.2`	32	`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
`Qwen/Qwen3.5-397B-A17B`	32	same
`Qwen/Qwen3.6-35B-A3B`	32	same
`MiniMaxAI/MiniMax-M3`	32	same

The eligible set can change over time. Call GET /v1/models to confirm what your key can reach, and register only against models from the list above.

Adapter requirements

Train with PEFT and export the two standard adapter files:

adapter_config.json with peft_type set to "LORA", task_type set to "CAUSAL_LM", base_model_name_or_path matching the base model you register against, an adapter rank r no greater than the base model’s max rank, and target_modules within the base model’s allowed set.
adapter_model.safetensors with the adapter weights.

Each file may be up to 5 GiB. The config file name must end in .json and the weights file name must end in .safetensors.

Register an adapter

Upload the adapter files

Upload both files to Valar with purpose set to lora. The Files endpoint is OpenAI-compatible, so the OpenAI SDK works directly.

from openai import OpenAI

client = OpenAI(base_url="https://api.valarhq.ai/v1", api_key="YOUR_VALAR_KEY")

with open("adapter_config.json", "rb") as f:
    cfg = client.files.create(file=f, purpose="lora")
with open("adapter_model.safetensors", "rb") as f:
    wts = client.files.create(file=f, purpose="lora")

Each upload returns a file object with an id you pass to the next step.

Create the LoRA

Register the adapter against one or more supported base models. name must be 2 to 64 characters, lowercase alphanumeric or dashes, start and end with an alphanumeric character, and be unique within your organization.

curl https://api.valarhq.ai/v1/loras \
  -H "Authorization: Bearer YOUR_VALAR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "support-tone-v1",
    "supported_models": ["zai-org/GLM-5.2"],
    "config_file_id": "file-abc123",
    "weights_file_id": "file-def456",
    "display_name": "Support tone v1",
    "description": "House support voice, trained on resolved tickets"
  }'

The response is a LoRA object created in the verifying status:

{
  "id": "lora-9f2c1a7b",
  "object": "lora",
  "name": "support-tone-v1",
  "display_name": "Support tone v1",
  "description": "House support voice, trained on resolved tickets",
  "supported_models": ["zai-org/GLM-5.2"],
  "config_file_id": "file-abc123",
  "weights_file_id": "file-def456",
  "status": "verifying",
  "created_at": 1731000000,
  "updated_at": 1731000000
}

Validation and status

Every adapter is reviewed before it can be used. A LoRA moves through three states:

Status	Meaning
`verifying`	Registered and awaiting validation.
`deployed`	Validated and ready.
`failed`	Validation failed. `status_details` carries the reason.

Manage adapters

List every adapter in your organization, fetch one by name or id, or delete one.

# List
curl https://api.valarhq.ai/v1/loras -H "Authorization: Bearer YOUR_VALAR_KEY"

# Fetch by name or by id
curl https://api.valarhq.ai/v1/loras/support-tone-v1 -H "Authorization: Bearer YOUR_VALAR_KEY"

# Delete
curl -X DELETE https://api.valarhq.ai/v1/loras/support-tone-v1 -H "Authorization: Bearer YOUR_VALAR_KEY"

GET /v1/loras returns { "object": "list", "data": [ ... ] }. A delete returns { "id": "...", "object": "lora.deleted", "deleted": true }.

Using a LoRA (coming soon)

Once an adapter is deployed, you will be able to run it by referencing it on a request to a supported base model. This is not yet available. When it ships, this section will document the exact request field and any completion-window constraints.

Getting started

Build agents

Run in production

ValarCode

Platform

LoRA adapters

Entitlement

Supported base models

Adapter requirements

Register an adapter

Validation and status

Manage adapters

Using a LoRA (coming soon)

​Entitlement

​Supported base models

​Adapter requirements

​Register an adapter

​Validation and status

​Manage adapters

​Using a LoRA (coming soon)

Entitlement

Supported base models

Adapter requirements

Register an adapter

Validation and status

Manage adapters

Using a LoRA (coming soon)