Microsoft LogoPhi 3 Mini 128K Instruct

A highly capable LLM with just 3.8 billion parameters and a 128K-token context window

Deploy Phi 3 Mini 128K Instruct behind an API endpoint in seconds.

Deploy model

Example usage

Phi 3 uses the standard set of LLM parameters and has optional streaming output.

Input
1import requests
2
3# Replace the empty string with your model id below
4model_id = ""
5baseten_api_key = os.environ["BASETEN_API_KEY"]
6
7messages = [
8    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
9    {"role": "user", "content": "Who are you?"},
10]
11data = {
12    "messages": messages,
13    "stream": True,
14    "max_new_tokens": 512,
15    "temperature": 0.5
16}
17
18# Call model endpoint
19res = requests.post(
20    f"https://model-{model_id}.api.baseten.co/production/predict",
21    headers={"Authorization": f"Api-Key {baseten_api_key}"},
22    json=data,
23    stream=True
24)
25
26# Print the generated tokens as they get streamed
27for content in res.iter_content():
28    print(content.decode("utf-8"), end="", flush=True)
JSON output
1[
2    "arrrg",
3    "me hearty",
4    "I",
5    "be",
6    "doing",
7    "..."
8]

Deploy any model in just a few commands

Avoid getting tangled in complex deployment processes. Deploy best-in-class open-source models and take advantage of optimized serving for your own models.

$

truss init -- example stable-diffusion-2-1-base ./my-sd-truss

$

cd ./my-sd-truss

$

export BASETEN_API_KEY=MdNmOCXc.YBtEZD0WFOYKso2A6NEQkRqTe

$

truss push

INFO

Serializing Stable Diffusion 2.1 truss.

INFO

Making contact with Baseten πŸ‘‹ πŸ‘½

INFO

πŸš€ Uploading model to Baseten πŸš€

Upload progress: 0% | | 0.00G/2.39G