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Animica Train — ML Model Fine-Tuning via x402
x402
Animica Train — ML Model Fine-Tuning via x402 is a paid API for AI agents from animica.dev, paid per call via x402, $1/call, status unknown (last checked 2026-09-15).
Fine-tune or extend an Animica ML model (linear head or LoRA head) on a labeled dataset, paying per training run via x402.
Price / call$1
Success—
StatusUnknown
How this service works
Animica Python Cloud: deploy a Python function to animica.dev, get a public endpoint, and earn ANM every time someone runs it. Free AI (OpenAI-compatible, no key), free scheduled Workers, and an 80/20 developer split.
Output
Returns a JSON object containing the trained model's ID, achieved metric score, training duration, and status. The model can subsequently be referenced by model_id for inference calls or further fine-tuning iterations.
Example use cases
Custom spam classifier training
“I have a labeled dataset of 200 email snippets tagged as spam or not-spam — can you train a linear_head classifier on Animica targeting 90% accuracy within one hour, with a $1 USDC budget?”
LoRA fine-tuning on domain text
“Fine-tune an existing Animica model (model ID: anm-base-v2) using LoRA on my medical Q&A dataset — 50 text-label pairs — aiming for 0.88 accuracy, max 45 minutes, budget $1 USDC.”
Iterative model improvement pipeline
“Take my current Animica model anm-classifier-7 and retrain it with my new batch of customer support tickets and resolution labels using a linear_head task, targeting 95% accuracy in under 30 minutes.”
Technical details
Endpoint
POSThttps://animica.dev/x402/trainWhen to prefer
Choose this endpoint when you need to fine-tune or adapt an ML model on your own labeled dataset without managing GPU infrastructure, and want pay-per-call pricing with no upfront commitment. Prefer it over generic cloud ML platforms when you want to build on an existing Animica model baseline, need crypto-native payment (USDC via x402), or want to chain training with other Animica services like inference, blob storage, or prediction markets in a single agent workflow.
Side effects
- Insufficient budget: training job exceeds the specified USDC amount before reaching target metric
- Invalid task type: task field must be exactly 'linear_head' or 'lora_head'
- Dataset format error: texts and labels arrays must be non-empty and equal length
- Timeout: training exceeds max_duration_seconds without reaching target_score
- Invalid model_id: referenced base model does not exist or is inaccessible
- Payment failure: x402 payment of $1 USDC not confirmed before job starts
- Stale quote_id: quote has expired and must be refreshed from /x402/train/quote
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| task | string | linear_head | lora_head |
| budget | object | {"amount":"1.00","currency":"USDC"} |
| dataset | object | {"texts":[...],"labels":[...]} |
| model_id | string | improve an existing Animica model instead of creating one; the baseline becomes that model |
| quote_id | string | a quote from POST /x402/train/quote; without one the request is priced at the default budget |
| requirements | object | {"target_metric":"accuracy","target_score":0.92,"max_duration_seconds":3600} |
Output
Schema · Response
{
"type": "json"
}Tags
x402
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