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‎.github/workflows/check_code_quality.yml‎

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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v3
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uses: actions/checkout@v4
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- uses: pre-commit/action@v3.0.1

‎.github/workflows/upload-to-pypi.yml‎

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python-version: ["3.10"]
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steps:
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- uses: actions/checkout@v2
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- uses: actions/checkout@v4
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v2.2.2
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with:

‎README.md‎

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@@ -52,25 +52,101 @@ This repo contains both a Python Library and a CLI. We'll demonstrate how to use
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### Chat Completions
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```python
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import os
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from together import Together
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client = Together(api_key=os.environ.get("TOGETHER_API_KEY"))
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client = Together()
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# Simple text message
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response = client.chat.completions.create(
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model="mistralai/Mixtral-8x7B-Instruct-v0.1",
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messages=[{"role": "user", "content": "tell me about new york"}],
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)
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print(response.choices[0].message.content)
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# Multi-modal message with text and image
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response = client.chat.completions.create(
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model="meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo",
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messages=[{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "What's in this image?"
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},
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{
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"type": "image_url",
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"image_url": {
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"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"
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}
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}
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]
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}]
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)
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print(response.choices[0].message.content)
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# Multi-modal message with multiple images
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response = client.chat.completions.create(
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model="Qwen/Qwen2.5-VL-72B-Instruct",
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messages=[{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "Compare these two images."
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},
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{
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"type": "image_url",
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"image_url": {
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"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"
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}
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},
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{
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"type": "image_url",
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"image_url": {
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"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/slack.png"
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}
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}
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]
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}]
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)
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print(response.choices[0].message.content)
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# Multi-modal message with text and video
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response = client.chat.completions.create(
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model="Qwen/Qwen2.5-VL-72B-Instruct",
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messages=[{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "What's happening in this video?"
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},
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{
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"type": "video_url",
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"video_url": {
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"url": "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerFun.mp4"
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}
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}
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]
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}]
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)
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print(response.choices[0].message.content)
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```
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The chat completions API supports three types of content:
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- Plain text messages using the `content` field directly
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- Multi-modal messages with images using `type: "image_url"`
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- Multi-modal messages with videos using `type: "video_url"`
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When using multi-modal content, the `content` field becomes an array of content objects, each with its own type and corresponding data.
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#### Streaming
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```python
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import os
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from together import Together
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client = Together(api_key=os.environ.get("TOGETHER_API_KEY"))
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client = Together()
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stream = client.chat.completions.create(
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model="mistralai/Mixtral-8x7B-Instruct-v0.1",
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messages=[{"role": "user", "content": "tell me about new york"}],
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#### Async usage
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```python
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import os, asyncio
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import asyncio
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from together import AsyncTogether
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async_client = AsyncTogether(api_key=os.environ.get("TOGETHER_API_KEY"))
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async_client = AsyncTogether()
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messages = [
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"What are the top things to do in San Francisco?",
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"What country is Paris in?",
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]
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async def async_chat_completion(messages):
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async_client = AsyncTogether(api_key=os.environ.get("TOGETHER_API_KEY"))
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async_client = AsyncTogether()
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tasks = [
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async_client.chat.completions.create(
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model="mistralai/Mixtral-8x7B-Instruct-v0.1",
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Completions are for code and language models shown [here](https://docs.together.ai/docs/inference-models). Below, a code model example is shown.
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```python
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import os
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from together import Together
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client = Together(api_key=os.environ.get("TOGETHER_API_KEY"))
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client = Together()
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response = client.completions.create(
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model="codellama/CodeLlama-34b-Python-hf",
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#### Streaming
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```python
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import os
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from together import Together
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client = Together(api_key=os.environ.get("TOGETHER_API_KEY"))
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client = Together()
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stream = client.completions.create(
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model="codellama/CodeLlama-34b-Python-hf",
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prompt="Write a Next.js component with TailwindCSS for a header component.",
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#### Async usage
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```python
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import os, asyncio
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import asyncio
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from together import AsyncTogether
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async_client = AsyncTogether(api_key=os.environ.get("TOGETHER_API_KEY"))
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async_client = AsyncTogether()
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prompts = [
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"Write a Next.js component with TailwindCSS for a header component.",
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"Write a python function for the fibonacci sequence",
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### Image generation
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```python
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import os
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from together import Together
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client = Together(api_key=os.environ.get("TOGETHER_API_KEY"))
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client = Together()
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response = client.images.generate(
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prompt="space robots",
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from typing import List
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from together import Together
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client = Together(api_key=os.environ.get("TOGETHER_API_KEY"))
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client = Together()
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def get_embeddings(texts: List[str], model: str) -> List[List[float]]:
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texts = [text.replace("\n", " ") for text in texts]
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from typing import List
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from together import Together
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client = Together(api_key=os.environ.get("TOGETHER_API_KEY"))
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client = Together()
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def get_reranked_documents(query: str, documents: List[str], model: str, top_n: int = 3) -> List[str]:
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outputs = client.rerank.create(model=model, query=query, documents=documents, top_n=top_n)
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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](https://docs.together.ai/docs/fine-tuning-python).
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```python
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import os
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from together import Together
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client = Together(api_key=os.environ.get("TOGETHER_API_KEY"))
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client = Together()
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client.files.upload(file="somedata.jsonl") # uploads a file
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client.files.list() # lists all uploaded files
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The 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](https://docs.together.ai/docs/fine-tuning-python).
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```python
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import os
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from together import Together
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client = Together(api_key=os.environ.get("TOGETHER_API_KEY"))
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client = Together()
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client.fine_tuning.create(
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training_file = 'file-d0d318cb-b7d9-493a-bd70-1cfe089d3815',
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This lists all the models that Together supports.
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```python
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from together import Together
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client = Together(api_key=os.environ.get("TOGETHER_API_KEY"))
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client = Together()
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models = client.models.list()
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