LoRAs

Upload PEFT-trained LoRAs for supported base models. After upload, give the LoRA a name and pass that name or ID in request metadata. If you train LoRAs with Tinker, you can also sample directly from Tinker checkpoints without uploading. See Tinker.

Supported base models

LoRA serving is available for:

Base model	Max rank	Target modules
`moonshotai/Kimi-K2.6`	32	all

If a LoRA is incompatible with the base model or exceeds the rank limit, validation or inference fails.

Adapter requirements

Train with PEFT and export the two standard files:

adapter_config.json
adapter_model.safetensors

Use adapter config values compatible with the base model:

base_model_name_or_path should identify the base model you register in supported_models (e.g. moonshotai/Kimi-K2.6).
peft_type should be "LORA".
task_type should be "CAUSAL_LM".
r (rank) must be no more than the base model’s max rank.
target_modules can include any modules supported by the base model and runtime. Sail does not restrict Kimi K2.6 LoRAs to a fixed target-module allowlist.

Other adapter-config fields (lora_alpha, lora_dropout, bias settings, etc.) are preserved as-is. The adapter_config.json and adapter_model.safetensors files can each be up to 100 GiB.

Add a LoRA

Create a LoRA in three steps: upload the config file, upload the weights file, then call POST /v1/loras. The response includes validation records for each requested model.

1. Upload the two adapter files

Use POST /v1/files (multipart):

from openai import OpenAI

client = OpenAI(base_url="https://api.sailresearch.com/v1", api_key="YOUR_KEY")

with open("my-adapter/adapter_config.json", "rb") as f:
    cfg = client.files.create(file=f, purpose="lora")

with open("my-adapter/adapter_model.safetensors", "rb") as f:
    wts = client.files.create(file=f, purpose="lora")

2. Create the LoRA

import requests

resp = requests.post(
    "https://api.sailresearch.com/v1/loras",
    headers={"Authorization": "Bearer YOUR_KEY"},
    json={
        "name": "funnier-v1",
        "supported_models": ["moonshotai/Kimi-K2.6"],
        "config_file_id": cfg.id,
        "weights_file_id": wts.id,
        # optional
        "display_name": "Funnier v1",
        "description": "Fine-tuned on a standup-comedy corpus to write punchier, joke-forward replies.",
    },
    timeout=30,
)
resp.raise_for_status()
lora = resp.json()
print(lora["id"], lora["status"])  # e.g. 3fa85f64-... pending_validation

Each supported_models entry must be a known Sail model ID. Naming rules for the name field:

2–64 characters
lowercase alphanumeric or dashes ([a-z0-9-])
must start and end with an alphanumeric character
unique within your organization (duplicate → 409)

The file IDs you pass must belong to the same organization as your API key.

Validation flow

POST /v1/loras creates one validation record per supported_models entry. Each record appears in the response under validations:

{
  "id": "3fa85f64-...",
  "name": "funnier-v1",
  "status": "pending_validation",
  "supported_models": ["moonshotai/Kimi-K2.6"],
  "validations": [
    {
      "model": "moonshotai/Kimi-K2.6",
      "status": "pending",
      "response_id": "resp_..."
    }
  ]
}

Sail validates the LoRA on each model in supported_models. Poll GET /v1/loras/{name} until validation finishes for the model you want to use.

Validation status	Meaning
`pending`	The validation task has been created.
`running`	The validation request is in progress.
`succeeded`	The LoRA loaded and completed a validation request for that model.
`failed`	The LoRA is not usable for that model. `result_code` and `result_message` describe the failure.
`unverified`	Sail could not complete validation automatically.

Read validations for model-specific status. Requests using a LoRA are rejected only when the latest validation for that model is failed; pending, running, and unverified records remain usable. If you later add a model with PATCH /v1/loras/{name}, Sail creates validation records for newly added models that have not already succeeded validation.

3. Fetch LoRAs

# by name
curl -H "Authorization: Bearer $SAIL_API_KEY" https://api.sailresearch.com/v1/loras/funnier-v1

# by id
curl -H "Authorization: Bearer $SAIL_API_KEY" https://api.sailresearch.com/v1/loras/3fa85f64-...

# list all loras for your org
curl -H "Authorization: Bearer $SAIL_API_KEY" https://api.sailresearch.com/v1/loras

Use a LoRA

Pass the LoRA’s name (or its UUID) as metadata.lora on any Responses, Chat Completions, or Messages request. model must be one of the LoRA’s supported_models:

response = client.responses.create(
    model="moonshotai/Kimi-K2.6",
    input=[{"role": "user", "content": "Write a one-liner about a centrifuge that's having a bad day."}],
    metadata={
        "lora": "funnier-v1",
        "completion_window": "balanced",
    },
    background=True,
)

Chat Completions:

response = client.chat.completions.create(
    model="moonshotai/Kimi-K2.6",
    messages=[{"role": "user", "content": "Tell me a joke about Go channels."}],
    extra_body={"metadata": {"lora": "funnier-v1", "completion_window": "balanced"}},
)

Constraints on LoRA requests

Use the balanced or flex completion window for Kimi K2.6 LoRA requests. SailTokenCompleter uses balanced by default. asap is not available for LoRA requests. For more on completion windows, see Completion Windows.
model must be in the LoRA’s supported_models list. Requesting a different base model returns 400.
A failed model validation blocks that model. GET /v1/loras/{name} includes validations[].result_message when validation fails.
The LoRA must belong to your organization. Names are scoped per-org; two orgs can independently own a LoRA called funnier-v1.
You can use either the LoRA’s name or its UUID in metadata.lora.

Getting started

Inference

Sailboxes

Guides

Voyages

Security & compliance

Supported base models

Adapter requirements

Add a LoRA

1. Upload the two adapter files

2. Create the LoRA

Validation flow

3. Fetch LoRAs

Use a LoRA

Constraints on LoRA requests

​Supported base models

​Adapter requirements

​Add a LoRA

​1. Upload the two adapter files

​2. Create the LoRA

​Validation flow

​3. Fetch LoRAs

​Use a LoRA

​Constraints on LoRA requests

Supported base models

Adapter requirements

Add a LoRA

1. Upload the two adapter files

2. Create the LoRA

Validation flow

3. Fetch LoRAs

Use a LoRA

Constraints on LoRA requests