Fine-tune Nemotron 3.5 Lightning on Pioneer

Pioneer supports parameter-efficient (LoRA) post-training on the Nemotron 3.5 Lightning family. You bring your training data, choose the general, finance, or healthcare target, and Pioneer handles the infrastructure, routing, and serving. The result is a fine-tuned adapter you can call over the same API, with no GPU management required. New decoder training jobs use supervised fine-tuning (SFT) through the POST /felix/training-jobs endpoint.

Training method

SFT trains the model to imitate the assistant turns in your examples. Supply chat-format messages with the user instruction and desired assistant response.

Decoder SFT is LoRA-based. A completed job produces a low-rank adapter that is hot-swapped onto the shared base model at serve time and exposed behind the same inference endpoints as base models — reference the training job’s id as the model_id at inference time. training_type defaults to "lora" and is the only supported value for decoder LLMs; "full" is reserved for GLiNER encoder models.

End-to-end walkthrough

Choose a decoder base model

Use GET /base-models to see the full current catalog, filtered to models that support training:

curl "https://api.pioneer.ai/base-models?task_type=decoder&supports_training=true" \
  -H "X-API-Key: YOUR_API_KEY"

The supported decoder targets are:

Model ID	Label	Context
`nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16`	Nemotron 3.5 Lightning 30B-A3B	8K
`fastino/Fastino-Nemotron-3.5-Lightning-Finance`	Fastino Nemotron 3.5 Lightning Finance	8K
`fastino/Fastino-Nemotron-3.5-Lightning-Healthcare`	Fastino Nemotron 3.5 Lightning Healthcare	8K

Use the general Lightning target unless your data is specifically finance or healthcare. These targets have an 8K qualified context window, so split or truncate longer examples before training.All listed decoder targets support LoRA SFT.

Prepare your training data

Format each example as a chat conversation with an assistant response:

# Each row: {"messages": [{"role": "user" | "assistant" | "system", "content": "..."}]}
# Generate synthetically:
curl -X POST https://api.pioneer.ai/generate \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "task_type": "decoder",
    "dataset_name": "my-sft-dataset",
    "num_examples": 200,
    "domain_description": "Customer support for a SaaS product"
  }'

See the Synthetic Data guide for the full set of /generate options, including auto-labelling existing text. Once generated or uploaded, wait until the dataset status is ready before starting training.

Start a training job

Submit your SFT job with POST /felix/training-jobs:

curl -X POST https://api.pioneer.ai/felix/training-jobs \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "my-sft-model",
    "base_model": "nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16",
    "training_type": "lora",
    "datasets": [{"name": "my-sft-dataset", "version": "1"}],
    "lora_r": 16,
    "lora_alpha": 32,
    "learning_rate": 2e-5,
    "nr_epochs": 3
  }'

Pioneer routes your job automatically to the best available provider. The response includes your job ID:

{ "id": "uuid-of-training-job", "status": "requested" }

Poll until training is complete

Check job status by polling GET /felix/training-jobs/:id.

curl https://api.pioneer.ai/felix/training-jobs/YOUR_JOB_ID \
  -H "X-API-Key: YOUR_API_KEY"

Status transitions: requested → running → complete → deployed (or failed / stopped). The terminal success state is deployed, reached automatically once the adapter is live behind the inference endpoints.You can also stream training logs while the job is running:

curl https://api.pioneer.ai/felix/training-jobs/YOUR_JOB_ID/logs \
  -H "X-API-Key: YOUR_API_KEY"

Run inference on your fine-tuned model

Once the job status is deployed, use your job ID as the model_id (or model) on any of the three inference interfaces.Pioneer native API — use "task": "generate" for decoder models:

curl -X POST https://api.pioneer.ai/inference \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model_id": "YOUR_JOB_ID",
    "task": "generate",
    "messages": [{"role": "user", "content": "Summarize this article: ..."}]
  }'

OpenAI-compatible endpoint — drop-in replacement for the OpenAI SDK:

from openai import OpenAI

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

response = client.chat.completions.create(
    model="YOUR_JOB_ID",
    messages=[{"role": "user", "content": "Summarize this article: ..."}]
)
print(response.choices[0].message.content)

curl -X POST https://api.pioneer.ai/v1/chat/completions \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "YOUR_JOB_ID",
    "messages": [{"role": "user", "content": "Summarize this article: ..."}]
  }'

Anthropic-compatible endpoint:

curl -X POST https://api.pioneer.ai/v1/messages \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "YOUR_JOB_ID",
    "max_tokens": 1024,
    "messages": [{"role": "user", "content": "Summarize this article: ..."}]
  }'

Streaming is supported on all three interfaces.

Downloading your trained model weights is available on the Pro plan and above. Use GET /felix/training-jobs/:id/download to retrieve the weights once training is complete.

LoRA hyperparameters

LoRA capacity and the core optimization settings are configurable; the defaults are sensible starting points for SFT.

Field	Default	Purpose
`lora_r`	`16`	LoRA rank — adapter capacity. Raise it for harder tasks or larger datasets.
`lora_alpha`	`32`	LoRA scaling factor (typically ~2× `lora_r`).
`lora_dropout`	`0.1`	Dropout applied to the adapter during training.
`learning_rate`	`2e-5`	Peak AdamW learning rate.
`batch_size`	`4`	Per-step batch size.
`nr_epochs`	`100`	Epoch ceiling; early stopping usually halts well before this.
`validation_data_percentage`	`0.2`	Fraction of the dataset held out for validation.

Supported models

The only decoder training targets are:

Base model	Intended domain
`nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16`	General-purpose decoder training
`fastino/Fastino-Nemotron-3.5-Lightning-Finance`	Finance
`fastino/Fastino-Nemotron-3.5-Lightning-Healthcare`	Healthcare

GLiNER2 Base, Large, Multi, and Multi Large encoder targets are also supported through the same endpoint. See the encoder fine-tuning guides for NER, classification, and structured extraction. Use GET /base-models?supports_training=true immediately before submitting a job. It is the live source of truth for the training targets and algorithms available to your workspace.

Serverless inference for base models

If you want to run inference on a base model without fine-tuning, use one of the supported inference families:

Model ID	Label	Context
`nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16`	Nemotron 3.5 Lightning 30B-A3B	8K
`deepseek-ai/DeepSeek-V4-Flash`	DeepSeek V4 Flash	See live catalog
`zai-org/GLM-5.2`	GLM 5.2	See live catalog
`claude-opus-5`	Claude Opus 5	See live catalog
`claude-sonnet-5`	Claude Sonnet 5	See live catalog
`claude-haiku-4-5`	Claude Haiku 4.5	See live catalog
`gpt-5.5`	GPT-5.5	See live catalog
`gpt-5.6-terra`	GPT-5.6 Terra	See live catalog

Use GET /base-models?task_type=decoder&supports_inference=true to see the current serverless catalog.

Next steps

Synthetic Data — generate training data without manual annotation
Adaptive Inference — automatically retrain on live production data
Agent Skills — let an AI coding agent manage training and inference for you
Training Jobs API — every endpoint, parameter, and response field

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Changelog

Fine-tune Nemotron 3.5 Lightning on Pioneer

Training method

End-to-end walkthrough

LoRA hyperparameters

Supported models

Serverless inference for base models

Next steps

​Training method

​End-to-end walkthrough

​LoRA hyperparameters

​Supported models

​Serverless inference for base models

​Next steps

Training method

End-to-end walkthrough

LoRA hyperparameters

Supported models

Serverless inference for base models

Next steps