Note
Check out the new SDK: together-py
📖 Migration Guide: https://docs.together.ai/docs/pythonv2-migration-guide
Using uv (Recommended):
# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh
# Install together python SDK
uv add together --prerelease allow
# Or upgrade an existing installation
uv sync --upgrade-package together --prerelease allowUsing pip:
pip install --pre togetherThis package will be maintained until January 2026.
The Together Python API Library is the official Python client for Together's API platform, providing a convenient way for interacting with the REST APIs and enables easy integrations with Python 3.10+ applications with easy to use synchronous and asynchronous clients.
🚧 The Library was rewritten in v1.0.0 released in April of 2024. There were significant changes made.
To install Together Python Library from PyPI, simply run:
pip install --upgrade together🚧 You will need to create an account with Together.ai to obtain a Together API Key.
Once logged in to the Together Playground, you can find available API keys in this settings page.
export TOGETHER_API_KEY=xxxxxfrom together import Together
client = Together(api_key="xxxxx")This repo contains both a Python Library and a CLI. We'll demonstrate how to use both below.
from together import Together
client = Together()
# Simple text message
response = client.chat.completions.create(
model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
messages=[{"role": "user", "content": "tell me about new york"}],
)
print(response.choices[0].message.content)
# Multi-modal message with text and image
response = client.chat.completions.create(
model="meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo",
messages=[{
"role": "user",
"content": [
{
"type": "text",
"text": "What's in this image?"
},
{
"type": "image_url",
"image_url": {
"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"
}
}
]
}]
)
print(response.choices[0].message.content)
# Multi-modal message with multiple images
response = client.chat.completions.create(
model="Qwen/Qwen2.5-VL-72B-Instruct",
messages=[{
"role": "user",
"content": [
{
"type": "text",
"text": "Compare these two images."
},
{
"type": "image_url",
"image_url": {
"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"
}
},
{
"type": "image_url",
"image_url": {
"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/slack.png"
}
}
]
}]
)
print(response.choices[0].message.content)
# Multi-modal message with text and video
response = client.chat.completions.create(
model="Qwen/Qwen2.5-VL-72B-Instruct",
messages=[{
"role": "user",
"content": [
{
"type": "text",
"text": "What's happening in this video?"
},
{
"type": "video_url",
"video_url": {
"url": "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerFun.mp4"
}
}
]
}]
)
print(response.choices[0].message.content)The chat completions API supports three types of content:
- Plain text messages using the
contentfield directly - Multi-modal messages with images using
type: "image_url" - Multi-modal messages with videos using
type: "video_url"
When using multi-modal content, the content field becomes an array of content objects, each with its own type and corresponding data.
import os
from together import Together
client = Together()
stream = client.chat.completions.create(
model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
messages=[{"role": "user", "content": "tell me about new york"}],
stream=True,
)
for chunk in stream:
print(chunk.choices[0].delta.content or "", end="", flush=True)import asyncio
from together import AsyncTogether
async_client = AsyncTogether()
messages = [
"What are the top things to do in San Francisco?",
"What country is Paris in?",
]
async def async_chat_completion(messages):
async_client = AsyncTogether()
tasks = [
async_client.chat.completions.create(
model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
messages=[{"role": "user", "content": message}],
)
for message in messages
]
responses = await asyncio.gather(*tasks)
for response in responses:
print(response.choices[0].message.content)
asyncio.run(async_chat_completion(messages))Logprobs are logarithms of token-level generation probabilities that indicate the likelihood of the generated token based on the previous tokens in the context. Logprobs allow us to estimate the model's confidence in its outputs, which can be used to decide how to optimally consume the model's output (e.g. rejecting low confidence outputs, retrying or ensembling model outputs etc).
from together import Together
client = Together()
response = client.chat.completions.create(
model="meta-llama/Llama-3.2-3B-Instruct-Turbo",
messages=[{"role": "user", "content": "tell me about new york"}],
logprobs=1
)
response_lobprobs = response.choices[0].logprobs
print(dict(zip(response_lobprobs.tokens, response_lobprobs.token_logprobs)))
# {'New': -2.384e-07, ' York': 0.0, ',': 0.0, ' also': -0.20703125, ' known': -0.20214844, ' as': -8.34465e-07, ... }More details about using logprobs in Together's API can be found here.
Completions are for code and language models shown here. Below, a code model example is shown.
from together import Together
client = Together()
response = client.completions.create(
model="codellama/CodeLlama-34b-Python-hf",
prompt="Write a Next.js component with TailwindCSS for a header component.",
max_tokens=200,
)
print(response.choices[0].text)from together import Together
client = Together()
stream = client.completions.create(
model="codellama/CodeLlama-34b-Python-hf",
prompt="Write a Next.js component with TailwindCSS for a header component.",
stream=True,
)
for chunk in stream:
print(chunk.choices[0].delta.content or "", end="", flush=True)import asyncio
from together import AsyncTogether
async_client = AsyncTogether()
prompts = [
"Write a Next.js component with TailwindCSS for a header component.",
"Write a python function for the fibonacci sequence",
]
async def async_chat_completion(prompts):
tasks = [
async_client.completions.create(
model="codellama/CodeLlama-34b-Python-hf",
prompt=prompt,
)
for prompt in prompts
]
responses = await asyncio.gather(*tasks)
for response in responses:
print(response.choices[0].text)
asyncio.run(async_chat_completion(prompts))from together import Together
client = Together()
response = client.images.generate(
prompt="space robots",
model="stabilityai/stable-diffusion-xl-base-1.0",
steps=10,
n=4,
)
print(response.data[0].b64_json)from typing import List
from together import Together
client = Together()
def get_embeddings(texts: List[str], model: str) -> List[List[float]]:
texts = [text.replace("\n", " ") for text in texts]
outputs = client.embeddings.create(model=model, input = texts)
return [outputs.data[i].embedding for i in range(len(texts))]
input_texts = ['Our solar system orbits the Milky Way galaxy at about 515,000 mph']
embeddings = get_embeddings(input_texts, model='togethercomputer/m2-bert-80M-8k-retrieval')
print(embeddings)from typing import List
from together import Together
client = Together()
def get_reranked_documents(query: str, documents: List[str], model: str, top_n: int = 3) -> List[str]:
outputs = client.rerank.create(model=model, query=query, documents=documents, top_n=top_n)
# sort by relevance score and returns the original docs
return [documents[i] for i in [x.index for x in sorted(outputs.results, key=lambda x: x.relevance_score, reverse=True)]]
query = "What is the capital of the United States?"
documents = ["New York","Washington, D.C.", "Los Angeles"]
reranked_documents = get_reranked_documents(query, documents, model='Salesforce/Llama-Rank-V1', top_n=1)
print(reranked_documents)Read more about Reranking here.
The files API is used for fine-tuning and allows developers to upload data to fine-tune on. It also has several methods to list all files, retrive files, and delete files. Please refer to our fine-tuning docs here.
from together import Together
client = Together()
client.files.upload(file="somedata.jsonl") # uploads a file
client.files.list() # lists all uploaded files
client.files.retrieve(id="file-d0d318cb-b7d9-493a-bd70-1cfe089d3815") # retrieves a specific file
client.files.retrieve_content(id="file-d0d318cb-b7d9-493a-bd70-1cfe089d3815") # retrieves content of a specific file
client.files.delete(id="file-d0d318cb-b7d9-493a-bd70-1cfe089d3815") # deletes a fileThe finetune API is used for fine-tuning and allows developers to create finetuning jobs. It also has several methods to list all jobs, retrive statuses and get checkpoints. Please refer to our fine-tuning docs here.
from together import Together
client = Together()
client.fine_tuning.create(
training_file = 'file-d0d318cb-b7d9-493a-bd70-1cfe089d3815',
model = 'meta-llama/Llama-3.2-3B-Instruct',
n_epochs = 3,
n_checkpoints = 1,
batch_size = "max",
learning_rate = 1e-5,
suffix = 'my-demo-finetune',
wandb_api_key = '1a2b3c4d5e.......',
)
client.fine_tuning.list() # lists all fine-tuned jobs
client.fine_tuning.retrieve(id="ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b") # retrieves information on finetune event
client.fine_tuning.cancel(id="ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b") # Cancels a fine-tuning job
client.fine_tuning.list_events(id="ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b") # Lists events of a fine-tune job
client.fine_tuning.download(id="ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b") # downloads compressed fine-tuned model or checkpoint to local diskThis lists all the models that Together supports.
from together import Together
client = Together()
models = client.models.list()
for model in models:
print(model)The batch API allows you to submit larger inference jobs for completion with a 24 hour turn-around time, below is an example. To learn more refer to the docs here.
from together import Together
client = Together()
# Upload the batch file
batch_file = client.files.upload(file="simpleqa_batch_student.jsonl", purpose="batch-api")
# Create the batch job
batch = client.batches.create_batch(file_id=batch_file.id, endpoint="/v1/chat/completions")
# Monitor the batch status
batch_stat = client.batches.get_batch(batch.id)
# List all batches - contains other batches as well
client.batches.list_batches()
# Download the file content if job completed
if batch_stat.status == 'COMPLETED':
output_response = client.files.retrieve_content(id=batch_stat.output_file_id,
output="simpleqa_v3_output.jsonl")together chat.completions \
--message "system" "You are a helpful assistant named Together" \
--message "user" "What is your name?" \
--model meta-llama/Llama-4-Scout-17B-16E-InstructThe Chat Completions CLI enables streaming tokens to stdout by default. To disable streaming, use --no-stream.
together completions \
"Large language models are " \
--model meta-llama/Llama-4-Scout-17B-16E-Instruct \
--max-tokens 512 \
--stop "."The Completions CLI enables streaming tokens to stdout by default. To disable streaming, use --no-stream.
together images generate \
"space robots" \
--model stabilityai/stable-diffusion-xl-base-1.0 \
--n 4The image is opened in the default image viewer by default. To disable this, use --no-show.
# Help
together files --help
# Check file
together files check example.jsonl
# Upload file
together files upload example.jsonl
# List files
together files list
# Retrieve file metadata
together files retrieve file-6f50f9d1-5b95-416c-9040-0799b2b4b894
# Retrieve file content
together files retrieve-content file-6f50f9d1-5b95-416c-9040-0799b2b4b894
# Delete remote file
together files delete file-6f50f9d1-5b95-416c-9040-0799b2b4b894# Help
together fine-tuning --help
# Create fine-tune job
together fine-tuning create \
--model togethercomputer/llama-2-7b-chat \
--training-file file-711d8724-b3e3-4ae2-b516-94841958117d
# List fine-tune jobs
together fine-tuning list
# Retrieve fine-tune job details
together fine-tuning retrieve ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b
# List fine-tune job events
together fine-tuning list-events ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b
# Cancel running job
together fine-tuning cancel ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b
# Download fine-tuned model weights
together fine-tuning download ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b# Help
together models --help
# List models
together models listRefer to the Contributing Guide