Fine-tuning

Lab › Training turns a folder of chat examples into a model you own. You choose a base model, LoRA or full fine-tuning, how the result must be servable, and a price ceiling. AI Reserve validates the data, prices the run, places it on a training backend internally, and puts the finished weights back in your bucket. A provider is never an input: the quote hides the placement the way a GPU instance quote does.

Fine-tuning needs the AWS hub (the per-organization AWS account that also powers Elastic Container Service). Enabling it is one click on the Training page and takes a few minutes. Your training bucket, aireserve-training-<account id>, is created in that account the first time the page opens.

How it works

Connect AWS

An organization admin clicks Enable AWS hub on Lab › Training (or calls enable_containers over MCP). The account is vended by AI Reserve inside its AWS Organization; nothing else lives there. The page shows the lifecycle and updates on its own.

Add data

Create a dataset folder — datasets/<name>/ in your bucket — and put chat-format JSONL in it: one example per line, a messages array with user and assistant turns, the last turn by the assistant. Drop files on the page (a presigned S3 upload), or use the AWS CLI with a 12-hour session scoped to that one folder. Then Validate: AI Reserve pins the file’s S3 version, checks every line, and reports the example and token counts. Nothing trains until the data checks out.

Choose a model

Pick a base from the catalog (Llama 3.1 8B, Qwen3 8B, gpt-oss 20B, …), LoRA adapter or full fine-tune, and how you will use the result: a dedicated endpoint (per hour) or just the weights. Serverless per-token serving of trained adapters is not offered yet — no current backend serves them that way — and arrives with AI Reserve’s own multi-adapter lane. Training settings have sensible defaults and fold away; a setting outside the placed backend’s range is clamped and the change is shown on the quote.

Review and start

The quote shows the training price, an estimated duration, the serving price for the mode you chose, and your wallet balance after. The quote is signed and valid two minutes. Start reserves the estimate from your wallet; it is settled when the run succeeds and released if it fails or is cancelled.

Your run

Follow the run live. When it finishes the model card shows where the weights are (s3://…/models/<id>/), Download, and Deploy to a dedicated endpoint. A deployment’s model id is what you send to /v1/chat/completions.

Data format

{"messages":[{"role":"system","content":"You classify support tickets."},{"role":"user","content":"Please send my receipt for order 1182."},{"role":"assistant","content":"billing"}]}
{"messages":[{"role":"user","content":"The app crashes when I open settings."},{"role":"assistant","content":"bug"}]}

One JSON object per line, UTF-8, .jsonl.
Roles: system, user, assistant, tool, developer. Content is a string or an array of {type: "text", text} parts.
Every example needs at least one user and one assistant turn; the last turn must be the assistant’s (that is what the model learns).
At least 10 examples. A valid.jsonl beside train.jsonl is used as the validation split automatically.

Validation names the first problems by line number and never returns the examples themselves. Token counts are estimates for pricing; backends bill on their own tokenizer and the run records the actual.

Uploading with the AWS CLI

The page’s Use the AWS CLI instead fold mints a 12-hour session that can read and write one dataset folder and nothing else. The commands talk to S3 directly; nothing customer-sized passes through AI Reserve.

export AWS_ACCESS_KEY_ID=<from the page>
export AWS_SECRET_ACCESS_KEY=<from the page>
export AWS_SESSION_TOKEN=<from the page>
export AWS_DEFAULT_REGION=us-east-1

aws s3 cp ./train.jsonl s3://aireserve-training-123456789012/datasets/support-tickets/train.jsonl
aws s3 sync ./data        s3://aireserve-training-123456789012/datasets/support-tickets/
aws s3 ls                 s3://aireserve-training-123456789012/datasets/support-tickets/

The same credentials come from POST /v1/training/datasets/{datasetId}/session and aireserve training datasets session <id>.

LoRA or full, and what you get

	LoRA adapter	Full fine-tune
What is trained	A small set of extra weights on top of the base	Every weight
Result	`adapter_model.safetensors` (MBs) in your bucket	A complete checkpoint (GBs) in your bucket, or at the backend when above 1 GB
Serverless	Not yet (no current backend serves trained adapters serverless)	No — its own weights need their own hardware
Dedicated endpoint	Yes	Yes
Download	Yes	Yes
Training price	Lower	Higher (a surcharge over the LoRA rate)

The request shape is identical; only method differs. A combination that cannot be placed — full + serverless, a base nobody trains fully, a ceiling under the estimate — is refused at quote time with what would work, before any money moves.

API

Every step is a /v1/training call under your organization’s API key with compute read (quotes, reads) or manage (datasets, starts, deployments) access. The full reference is in the API reference.

# 1. a dataset folder, a presigned upload, validation
curl -X POST https://api.aireserve.com/v1/training/datasets \
  -H "Authorization: Bearer $AIRESERVE_API_KEY" -H "Content-Type: application/json" \
  -d '{"name":"support-tickets"}'
curl -X POST https://api.aireserve.com/v1/training/datasets/$DATASET/upload \
  -H "Authorization: Bearer $AIRESERVE_API_KEY" -H "Content-Type: application/json" \
  -d '{"filename":"train.jsonl","contentType":"application/jsonl"}'
# PUT the file bytes to the returned url, then:
curl -X POST https://api.aireserve.com/v1/training/datasets/$DATASET/validate \
  -H "Authorization: Bearer $AIRESERVE_API_KEY"

# 2. quote, then start from the quote
curl -X POST https://api.aireserve.com/v1/training/jobs/quote \
  -H "Authorization: Bearer $AIRESERVE_API_KEY" -H "Content-Type: application/json" \
  -d '{"name":"triage v1","datasetId":"'$DATASET'","baseModel":"meta-llama/llama-3.1-8b-instruct","method":"lora","serving":"dedicated","maxCostUsd":25}'
curl -X POST https://api.aireserve.com/v1/training/jobs \
  -H "Authorization: Bearer $AIRESERVE_API_KEY" -H "Content-Type: application/json" \
  -H "Idempotency-Key: triage-v1-$(date +%s)" \
  -d '{"quoteId":"'$QUOTE_ID'","name":"triage v1","costAcknowledged":true}'

# 3. follow, then deploy
curl https://api.aireserve.com/v1/training/jobs/$JOB -H "Authorization: Bearer $AIRESERVE_API_KEY"
curl -X POST https://api.aireserve.com/v1/training/deployments \
  -H "Authorization: Bearer $AIRESERVE_API_KEY" -H "Content-Type: application/json" \
  -d '{"modelId":"'$MODEL'","name":"triage-prod","kind":"dedicated","costAcknowledged":true}'

The CLI twin is aireserve training bases | datasets | jobs | models | deployments, and every step is an MCP operation (quote_training_run, start_training_run, …) for a coding agent on the AI Reserve connection.

Pricing

Training is priced per trained token (the validated token count × epochs) at the backend’s list rate, with a surcharge for full fine-tuning and a $1 minimum. The quote is the price: it is reserved from your wallet when you start, settled when the run succeeds, and released if it fails or is cancelled. A run that fails costs nothing.
Dedicated endpoints bill every hour they are up, from the first hour, at the rate the quote showed; stopping the deployment ends it.
Storage in your bucket is AWS’s S3 price, reconciled from the daily AWS cost check rather than metered by the hour; the bucket shows on My Resources › AWS.

When something goes wrong

Every refusal and failure is one plain sentence with the fix, never a provider’s raw text. Before any money moves, the quote refuses a combination that cannot be placed (409 no_placement) and says what would work; a hyperparameter outside the placed backend’s range is clamped and the change is listed on the quote (adjustments), never applied silently. A run that fails after starting is released from your wallet and the run shows one of:

What you see	What happened	What to do
”The provider could not read the dataset…”	The file passed our checks but the backend’s stricter validation refused it	Fix the line it names, re-validate
”The run ran out of GPU memory…”	Sequence length, rank or batch too large for the hardware	Lower max sequence length or LoRA rank
”Training became unstable…”	Loss went to NaN	Lower the learning rate
”The training hardware was reclaimed…”	Spot preemption at the backend	Start again; nothing was charged
”The training backend has no capacity right now” / “is having trouble”	429 / 5xx at the backend	Try again in a few minutes
”The training backend rejected our credentials” / “refused the run under its account limits”	Our provider account, not yours	The ops team is alerted; nothing was charged

Limits and notes

Dataset files up to a few GB each over the browser; larger sets go in with the CLI. Per-backend dataset limits are checked at quote time.
Weights above 1 GB (full checkpoints) stay with the training backend for now and serve from there; the model card says so.
Managed backends receive a copy of your pinned dataset file to train on and it is deleted when the run ends; the bucket is the system of record.
Objectives: supervised fine-tuning (SFT). Preference and reinforcement objectives are not offered yet.
Serving: dedicated endpoints and download. Serverless per-token serving of adapters is not offered until AI Reserve’s own multi-adapter lane ships; the request shape already carries serving: "serverless" for it.

Getting started

Connect your tool

GPU Infrastructure

Containers & orchestration

Capabilities

Migrate

Performance

Data & privacy

Reliability

How it works

Data format

Uploading with the AWS CLI

LoRA or full, and what you get

API

Pricing

When something goes wrong

Limits and notes

​How it works

​Data format

​Uploading with the AWS CLI

​LoRA or full, and what you get

​API

​Pricing

​When something goes wrong

​Limits and notes

How it works

Data format

Uploading with the AWS CLI

LoRA or full, and what you get

API

Pricing

When something goes wrong

Limits and notes