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Langtrain
Inject your proprietary data into open-source LLMs and deploy production-grade intelligence through CLI, SDKs, or Mac App.
The Problem
Public models were trained on the internet — not your business. Every inference drifts further from your domain.
LLMs forget everything after a context window. There's no persistent intelligence, no institutional knowledge.
Your SOPs, products, and data live in your systems. Base models have never seen them. They can't be prompted to know what they don't know.
Hallucinations, inconsistency, and latency at scale. Base models are research artifacts, not production infrastructure.
The Solution
Connect any data source — PDFs, CSVs, SQL, JSONL. Langtrain validates, structures, and prepares your dataset for high-fidelity fine-tuning.
LoRA, QLoRA, and full fine-tuning across 50+ open-source models. Runs on Apple Silicon, NVIDIA, or cloud. Own every weight.
One command to deploy a managed inference endpoint. Access via REST API, Python SDK, NPM package, or Mac App. No ops required.
How It Works
Drop your JSONL, CSV, or PDF. Langtrain validates schema and structure automatically.
langtrain inject ./data.jsonl ✓ 14,832 examples validated
Select a base model, configure LoRA rank and epochs. Training starts with a single command.
langtrain train --model llama-3 Epoch 3/3 loss: 0.142 ✓
Push your checkpoint to a managed inference endpoint. Live in under 60 seconds.
langtrain deploy ✓ Live → api.yourdomain.ai/v1
Call your model via REST, Python SDK, NPM package, or through the Mac App.
curl api.yourdomain.ai/v1/chat 200 OK · 94ms latency
Your model, any way you want it
import langtrain
client = langtrain.Client(api_key="lt_...")
response = client.chat.completions.create(
model="your-custom-model",
messages=[{"role": "user", "content": "Your query"}]
)
print(response.choices[0].message.content)Access Anywhere
Access your custom model through the Mac desktop app, CLI, Python SDK, or NPM package. Your deployment, your workflow.
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