> ## Documentation Index
> Fetch the complete documentation index at: https://docs.baseten.co/llms.txt
> Use this file to discover all available pages before exploring further.
# Build your first model
> Deploy a model to Baseten with just a config file. Pick an open-source model from Hugging Face, choose a GPU, and get an endpoint in minutes.
Baseten deploys models from a single `config.yaml` file. You point to a model on Hugging Face, choose a GPU, and Baseten builds a TensorRT-optimized container with an OpenAI-compatible API. No Python code, no Dockerfile, no container management.
This tutorial deploys [Qwen 2.5 3B Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct), a small but capable LLM, to a production-ready endpoint on an L4 GPU.
## Install and sign in
Before you begin, [sign up](https://app.baseten.co/signup) or [sign in](https://app.baseten.co/login) to Baseten, then install [uv](https://docs.astral.sh/uv/), a fast Python package manager.
Install the Truss CLI and connect it to your Baseten account. Browser login opens a tab to approve this device, so there's no API key to copy and paste.
**Install Truss**
```bash theme={"system"}
uv tool install truss
```
**Sign in**
```bash theme={"system"}
truss login --browser
```
Prefer not to install? Run `uvx truss login --browser` to use the same flow without a permanent install, and use `uvx truss …` for the rest of this guide.
## Create the config
Create a project directory with a `config.yaml`:
```bash theme={"system"}
mkdir qwen-2.5-3b && cd qwen-2.5-3b
```
Create a `config.yaml` file with the following contents:
```yaml config.yaml theme={"system"}
model_name: Qwen-2.5-3B
resources:
accelerator: L4
model_metadata:
tags:
- openai-compatible
trt_llm:
build:
base_model: decoder
checkpoint_repository:
source: HF
repo: "Qwen/Qwen2.5-3B-Instruct"
max_seq_len: 8192
quantization_type: fp8
tensor_parallel_count: 1
```
Each section of this config controls a different part of the deployment:
* `resources`: Selects an L4 GPU (24 GB VRAM) for inference.
* `trt_llm`: Tells Baseten to use [Engine-Builder-LLM](/engines/engine-builder-llm/overview), which compiles the model with TensorRT-LLM for optimized inference.
* `checkpoint_repository`: Points to the model weights on Hugging Face. Qwen 2.5 3B Instruct is ungated, so no access token is needed.
* `quantization_type: fp8`: Compresses weights to 8-bit floating point, significantly reducing memory usage with negligible quality loss.
## Deploy
Push to Baseten:
```bash theme={"system"}
truss push
```
You should see:
```output theme={"system"}
✨ Model Qwen 2.5 3B was successfully pushed ✨
🪵 View logs for your deployment at https://app.baseten.co/models/abc1d2ef/logs/xyz123
```
The logs URL contains your model ID, the string after `/models/` (for example, `abc1d2ef`). You'll need this to call the model's API. You can also find it in your [Baseten dashboard](https://app.baseten.co/models/).
Baseten now downloads the model weights, compiles them with TensorRT-LLM, and deploys the resulting container to an L4 GPU. You can watch progress in the logs linked above. When the deployment status shows "Active" in the dashboard, it's ready for requests.
## Call your model
Engine-based deployments serve an OpenAI-compatible API, so any code that works with the OpenAI SDK works with your model. Replace `{model_id}` with your model ID from the deployment output.
Install the OpenAI SDK if you don't have it:
```bash theme={"system"}
uv pip install openai
```
Create a chat completion:
```python theme={"system"}
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["BASETEN_API_KEY"],
base_url="https://model-{model_id}.api.baseten.co/environments/production/sync/v1",
)
response = client.chat.completions.create(
model="Qwen-2.5-3B",
messages=[
{"role": "user", "content": "What is machine learning?"}
],
)
print(response.choices[0].message.content)
```
```bash theme={"system"}
curl -s https://model-{model_id}.api.baseten.co/environments/production/sync/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Api-Key $BASETEN_API_KEY" \
-d '{
"model": "Qwen-2.5-3B",
"messages": [
{"role": "user", "content": "What is machine learning?"}
]
}'
```
You should see a response like:
```output theme={"system"}
Machine learning is a branch of artificial intelligence where systems learn
patterns from data to make predictions or decisions without being explicitly
programmed for each task...
```
## What just happened
With a single config file, you deployed a production-ready LLM endpoint. Here's what Baseten did:
1. Downloaded the Qwen 2.5 3B Instruct weights from Hugging Face.
2. Compiled the model with TensorRT-LLM, applying FP8 quantization for faster inference and lower memory usage.
3. Packaged everything into a container and deployed it to an L4 GPU.
4. Exposed an OpenAI-compatible API that handles tokenization, batching, and KV cache management automatically.
No `model.py`, no Docker setup, no inference server configuration. This config-only pattern works for most popular open-source LLMs, including Llama, Qwen, Mistral, Gemma, and Phi models.
## Next steps
Tune max sequence length, batch size, quantization, and runtime settings for your deployment.
Add custom Python when you need preprocessing, postprocessing, or unsupported model architectures.
Configure replicas, concurrency targets, and scale-to-zero for production traffic.
Move from development to production with `truss push --promote`.