Together AI
Installation
# Install Together Python client
pip install together
# Set API key
export TOGETHER_API_KEY="xxxxxxxxxxxx"
Chat Completions (OpenAI-Compatible)
# chat_completions.py — Use Together AI with the OpenAI SDK
from openai import OpenAI
client = OpenAI(
base_url="https://api.together.xyz/v1",
api_key="your-together-api-key",
)
response = client.chat.completions.create(
model="meta-llama/Llama-3.1-70B-Instruct-Turbo",
messages=[
{"role": "system", "content": "You are a helpful coding assistant."},
{"role": "user", "content": "Write a Python function to merge two sorted lists."},
],
max_tokens=500,
temperature=0.7,
)
print(response.choices[0].message.content)
Together Python SDK
# together_sdk.py — Use the native Together SDK for additional features
import together
client = together.Together()
# Chat completion
response = client.chat.completions.create(
model="mistralai/Mixtral-8x7B-Instruct-v0.1",
messages=[{"role": "user", "content": "Explain transformers architecture."}],
max_tokens=300,
)
print(response.choices[0].message.content)
# Streaming
stream = client.chat.completions.create(
model="meta-llama/Llama-3.1-8B-Instruct-Turbo",
messages=[{"role": "user", "content": "Write a haiku about coding"}],
stream=True,
)
for chunk in stream:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="", flush=True)
Embeddings
# embeddings.py — Generate embeddings for semantic search and RAG
from openai import OpenAI
client = OpenAI(
base_url="https://api.together.xyz/v1",
api_key="your-together-api-key",
)
response = client.embeddings.create(
model="togethercomputer/m2-bert-80M-8k-retrieval",
input=["What is machine learning?", "How does deep learning work?"],
)
for i, emb in enumerate(response.data):
print(f"Text {i}: {len(emb.embedding)} dimensions")
Image Generation
# image_gen.py — Generate images with open-source models
import together
import base64
client = together.Together()
response = client.images.generate(
model="stabilityai/stable-diffusion-xl-base-1.0",
prompt="A serene Japanese garden at sunset, watercolor style",
width=1024,
height=1024,
steps=30,
n=1,
)
# Save the image
image_data = base64.b64decode(response.data[0].b64_json)
with open("garden.png", "wb") as f:
f.write(image_data)
Fine-Tuning
# fine_tune.py — Fine-tune an open-source model on custom data
import together
client = together.Together()
# Upload training data (JSONL format)
file = client.files.upload(file="training_data.jsonl")
print(f"File ID: {file.id}")
# Start fine-tuning job
ft_job = client.fine_tuning.create(
training_file=file.id,
model="meta-llama/Llama-3.1-8B-Instruct-Reference",
n_epochs=3,
learning_rate=1e-5,
batch_size=4,
suffix="my-custom-model",
)
print(f"Job ID: {ft_job.id}")
# Check status
status = client.fine_tuning.retrieve(ft_job.id)
print(f"Status: {status.status}")
# training_data.jsonl — Training data format for fine-tuning
{"messages": [{"role": "system", "content": "You are a support agent."}, {"role": "user", "content": "How do I reset my password?"}, {"role": "assistant", "content": "Go to Settings > Security > Reset Password."}]}
{"messages": [{"role": "user", "content": "What are your hours?"}, {"role": "assistant", "content": "We're available 24/7 via chat and email."}]}
JSON Mode
# json_mode.py — Force structured JSON output from models
from openai import OpenAI
client = OpenAI(
base_url="https://api.together.xyz/v1",
api_key="your-together-api-key",
)
response = client.chat.completions.create(
model="meta-llama/Llama-3.1-70B-Instruct-Turbo",
messages=[{"role": "user", "content": "List 3 programming languages with their year of creation as JSON."}],
response_format={"type": "json_object"},
)
print(response.choices[0].message.content)
Key Concepts
- OpenAI-compatible: Use the OpenAI SDK by changing
base_url — minimal migration effort
- Open-source focus: Hosts Llama, Mistral, Mixtral, DBRX, and other open models
- Turbo models: Together-optimized versions with faster inference and lower latency
- Fine-tuning: LoRA and full fine-tuning on hosted models with simple JSONL data format
- Embeddings: Dedicated embedding models for RAG and semantic search use cases
- Pricing: Per-token pricing, typically cheaper than proprietary model APIs
eliferjunior