OpenAI-Compatible vLLM Serverless Endpoint Worker

Deploy OpenAI-Compatible Blazing-Fast LLM Endpoints powered by the vLLM Inference Engine on RunPod Serverless with just a few clicks.

News:

1. UI for Deploying vLLM Worker on RunPod console:

2. Worker vLLM 1.0.0preview with vLLM 0.4.2 now available under stable tags

Update 1.0.0preview is now available, use the image tag runpod/worker-vllm:dev-cuda12.1.0 or runpod/worker-vllm:dev-cuda11.8.0.

Main Changes:

• vLLM was updated from version 0.3.3 to 0.4.2, adding compatibility for Llama 3 and other models, as well as increasing performance.

We will soon be adding more features from the updates, such as multi-LoRA, multi-modality, and more.

3. Caching Accross RunPod Machines

Worker vLLM is now cached on all RunPod machines, resulting in near-instant deployment! Previously, downloading and extracting the image took 3-5 minutes on average.

Table of Contents

• Setting up the Serverless Worker
  • Option 1: Deploy Any Model Using Pre-Built Docker Image [RECOMMENDED]
    • Prerequisites
    • Environment Variables
      • LLM Settings
      • Tokenizer Settings
      • Tensor Parallelism (Multi-GPU) Settings
      • System Settings
      • Streaming Batch Size
      • OpenAI Settings
      • Serverless Settings
  • Option 2: Build Docker Image with Model Inside
    • Prerequisites
    • Arguments
    • Example: Building an image with OpenChat-3.5
      • (Optional) Including Huggingface Token
  • Compatible Model Architectures
• Usage: OpenAI Compatibility
  • Modifying your OpenAI Codebase to use your deployed vLLM Worker
  • OpenAI Request Input Parameters
    • Chat Completions
    • Completions
  • Examples: Using your RunPod endpoint with OpenAI
• Usage: standard
  • Input Request Parameters
    • Text Input Formats
    • Sampling Parameters

Setting up the Serverless Worker

Option 1: Deploy Any Model Using Pre-Built Docker Image [Recommended]

Note

You can now deploy from the dedicated UI on the RunPod console with all of the settings and choices listed. Try now by accessing in Explore or Serverless pages on the RunPod console!

We now offer a pre-built Docker Image for the vLLM Worker that you can configure entirely with Environment Variables when creating the RunPod Serverless Endpoint:

---

RunPod Worker Images

Below is a summary of the available RunPod Worker images, categorized by image stability and CUDA version compatibility.

CUDA Version	Stable Image Tag	Development Image Tag	Note
11.8.0	runpod/worker-vllm:stable-cuda11.8.0	runpod/worker-vllm:dev-cuda11.8.0	Available on all RunPod Workers without additional selection needed.
12.1.0	runpod/worker-vllm:stable-cuda12.1.0	runpod/worker-vllm:dev-cuda12.1.0	When creating an Endpoint, select CUDA Version 12.3, 12.2 and 12.1 in the filter.

---

Prerequisites

• RunPod Account

Environment Variables/Settings

Note: 0 is equivalent to False and 1 is equivalent to True for boolean values.

Name	Default	Type/Choices	Description
LLM Settings
MODEL_NAME*	-	str	Hugging Face Model Repository (e.g., openchat/openchat-3.5-1210).
MODEL_REVISION	None	str	Model revision(branch) to load.
MAX_MODEL_LEN	Model's maximum	int	Maximum number of tokens for the engine to handle per request.
BASE_PATH	/runpod-volume	str	Storage directory for Huggingface cache and model. Utilizes network storage if attached when pointed at /runpod-volume, which will have only one worker download the model once, which all workers will be able to load. If no network volume is present, creates a local directory within each worker.
LOAD_FORMAT	auto	str	Format to load model in.
HF_TOKEN	-	str	Hugging Face token for private and gated models.
QUANTIZATION	None	awq, squeezellm, gptq	Quantization of given model. The model must already be quantized.
TRUST_REMOTE_CODE	0	boolean as int	Trust remote code for Hugging Face models. Can help with Mixtral 8x7B, Quantized models, and unusual models/architectures.
SEED	0	int	Sets random seed for operations.
KV_CACHE_DTYPE	auto	auto, fp8_e5m2	Data type for kv cache storage. Uses DTYPE if set to auto.
DTYPE	auto	auto, half, float16, bfloat16, float, float32	Sets datatype/precision for model weights and activations.
Tokenizer Settings
TOKENIZER_NAME	None	str	Tokenizer repository to use a different tokenizer than the model's default.
TOKENIZER_REVISION	None	str	Tokenizer revision to load.
CUSTOM_CHAT_TEMPLATE	None	str of single-line jinja template	Custom chat jinja template. More Info
System, GPU, and Tensor Parallelism(Multi-GPU) Settings
GPU_MEMORY_UTILIZATION	0.95	float	Sets GPU VRAM utilization.
MAX_PARALLEL_LOADING_WORKERS	None	int	Load model sequentially in multiple batches, to avoid RAM OOM when using tensor parallel and large models.
BLOCK_SIZE	16	8, 16, 32	Token block size for contiguous chunks of tokens.
SWAP_SPACE	4	int	CPU swap space size (GiB) per GPU.
ENFORCE_EAGER	0	boolean as int	Always use eager-mode PyTorch. If False(0), will use eager mode and CUDA graph in hybrid for maximal performance and flexibility.
MAX_CONTEXT_LEN_TO_CAPTURE	8192	int	Maximum context length covered by CUDA graphs. When a sequence has context length larger than this, we fall back to eager mode.
DISABLE_CUSTOM_ALL_REDUCE	0	int	Enables or disables custom all reduce.
Streaming Batch Size Settings:
DEFAULT_BATCH_SIZE	50	int	Default and Maximum batch size for token streaming to reduce HTTP calls.
DEFAULT_MIN_BATCH_SIZE	1	int	Batch size for the first request, which will be multiplied by the growth factor every subsequent request.
DEFAULT_BATCH_SIZE_GROWTH_FACTOR	3	float	Growth factor for dynamic batch size.
The way this works is that the first request will have a batch size of DEFAULT_MIN_BATCH_SIZE, and each subsequent request will have a batch size of previous_batch_size * DEFAULT_BATCH_SIZE_GROWTH_FACTOR. This will continue until the batch size reaches DEFAULT_BATCH_SIZE. E.g. for the default values, the batch sizes will be 1, 3, 9, 27, 50, 50, 50, .... You can also specify this per request, with inputs max_batch_size, min_batch_size, and batch_size_growth_factor. This has nothing to do with vLLM's internal batching, but rather the number of tokens sent in each HTTP request from the worker
OpenAI Settings
RAW_OPENAI_OUTPUT	1	boolean as int	Enables raw OpenAI SSE format string output when streaming. Required to be enabled (which it is by default) for OpenAI compatibility.
OPENAI_SERVED_MODEL_NAME_OVERRIDE	None	str	Overrides the name of the served model from model repo/path to specified name, which you will then be able to use the value for the model parameter when making OpenAI requests
OPENAI_RESPONSE_ROLE	assistant	str	Role of the LLM's Response in OpenAI Chat Completions.
Serverless Settings
MAX_CONCURRENCY	300	int	Max concurrent requests per worker. vLLM has an internal queue, so you don't have to worry about limiting by VRAM, this is for improving scaling/load balancing efficiency
DISABLE_LOG_STATS	1	boolean as int	Enables or disables vLLM stats logging.
DISABLE_LOG_REQUESTS	1	boolean as int	Enables or disables vLLM request logging.

Tip

If you are facing issues when using Mixtral 8x7B, Quantized models, or handling unusual models/architectures, try setting TRUST_REMOTE_CODE to 1.

Option 2: Build Docker Image with Model Inside

To build an image with the model baked in, you must specify the following docker arguments when building the image.

Prerequisites

• RunPod Account
• Docker

Arguments:

• Required
  • MODEL_NAME
• Optional
  • MODEL_REVISION: Model revision to load (default: main).
  • BASE_PATH: Storage directory where huggingface cache and model will be located. (default: /runpod-volume, which will utilize network storage if you attach it or create a local directory within the image if you don't. If your intention is to bake the model into the image, you should set this to something like /models to make sure there are no issues if you were to accidentally attach network storage.)
  • QUANTIZATION
  • WORKER_CUDA_VERSION: 11.8.0 or 12.1.0 (default: 11.8.0 due to a small number of workers not having CUDA 12.1 support yet. 12.1.0 is recommended for optimal performance).
  • TOKENIZER_NAME: Tokenizer repository if you would like to use a different tokenizer than the one that comes with the model. (default: None, which uses the model's tokenizer)
  • TOKENIZER_REVISION: Tokenizer revision to load (default: main).

For the remaining settings, you may apply them as environment variables when running the container. Supported environment variables are listed in the Environment Variables section.

Example: Building an image with OpenChat-3.5

sudo docker build -t username/image:tag --build-arg MODEL_NAME="openchat/openchat_3.5" --build-arg BASE_PATH="/models" .

(Optional) Including Huggingface Token

If the model you would like to deploy is private or gated, you will need to include it during build time as a Docker secret, which will protect it from being exposed in the image and on DockerHub.

1. Enable Docker BuildKit (required for secrets).

export DOCKER_BUILDKIT=1

2. Export your Hugging Face token as an environment variable

export HF_TOKEN="your_token_here"

2. Add the token as a secret when building

docker build -t username/image:tag --secret id=HF_TOKEN --build-arg MODEL_NAME="openchat/openchat_3.5" .

Compatible Model Architectures

Below are all supported model architectures (and examples of each) that you can deploy using the vLLM Worker. You can deploy any model on HuggingFace, as long as its base architecture is one of the following:

• Aquila & Aquila2 (BAAI/AquilaChat2-7B, BAAI/AquilaChat2-34B, BAAI/Aquila-7B, BAAI/AquilaChat-7B, etc.)
• Baichuan & Baichuan2 (baichuan-inc/Baichuan2-13B-Chat, baichuan-inc/Baichuan-7B, etc.)
• BLOOM (bigscience/bloom, bigscience/bloomz, etc.)
• ChatGLM (THUDM/chatglm2-6b, THUDM/chatglm3-6b, etc.)
• Command-R (CohereForAI/c4ai-command-r-v01, etc.)
• DBRX (databricks/dbrx-base, databricks/dbrx-instruct etc.)
• DeciLM (Deci/DeciLM-7B, Deci/DeciLM-7B-instruct, etc.)
• Falcon (tiiuae/falcon-7b, tiiuae/falcon-40b, tiiuae/falcon-rw-7b, etc.)
• Gemma (google/gemma-2b, google/gemma-7b, etc.)
• GPT-2 (gpt2, gpt2-xl, etc.)
• GPT BigCode (bigcode/starcoder, bigcode/gpt_bigcode-santacoder, etc.)
• GPT-J (EleutherAI/gpt-j-6b, nomic-ai/gpt4all-j, etc.)
• GPT-NeoX (EleutherAI/gpt-neox-20b, databricks/dolly-v2-12b, stabilityai/stablelm-tuned-alpha-7b, etc.)
• InternLM (internlm/internlm-7b, internlm/internlm-chat-7b, etc.)
• InternLM2 (internlm/internlm2-7b, internlm/internlm2-chat-7b, etc.)
• Jais (core42/jais-13b, core42/jais-13b-chat, core42/jais-30b-v3, core42/jais-30b-chat-v3, etc.)
• LLaMA, Llama 2, and Meta Llama 3 (meta-llama/Meta-Llama-3-8B-Instruct, meta-llama/Meta-Llama-3-70B-Instruct, meta-llama/Llama-2-70b-hf, lmsys/vicuna-13b-v1.3, young-geng/koala, openlm-research/open_llama_13b, etc.)
• MiniCPM (openbmb/MiniCPM-2B-sft-bf16, openbmb/MiniCPM-2B-dpo-bf16, etc.)
• Mistral (mistralai/Mistral-7B-v0.1, mistralai/Mistral-7B-Instruct-v0.1, etc.)
• Mixtral (mistralai/Mixtral-8x7B-v0.1, mistralai/Mixtral-8x7B-Instruct-v0.1, mistral-community/Mixtral-8x22B-v0.1, etc.)
• MPT (mosaicml/mpt-7b, mosaicml/mpt-30b, etc.)
• OLMo (allenai/OLMo-1B-hf, allenai/OLMo-7B-hf, etc.)
• OPT (facebook/opt-66b, facebook/opt-iml-max-30b, etc.)
• Orion (OrionStarAI/Orion-14B-Base, OrionStarAI/Orion-14B-Chat, etc.)
• Phi (microsoft/phi-1_5, microsoft/phi-2, etc.)
• Phi-3 (microsoft/Phi-3-mini-4k-instruct, microsoft/Phi-3-mini-128k-instruct, etc.)
• Qwen (Qwen/Qwen-7B, Qwen/Qwen-7B-Chat, etc.)
• Qwen2 (Qwen/Qwen1.5-7B, Qwen/Qwen1.5-7B-Chat, etc.)
• Qwen2MoE (Qwen/Qwen1.5-MoE-A2.7B, Qwen/Qwen1.5-MoE-A2.7B-Chat, etc.)
• StableLM(stabilityai/stablelm-3b-4e1t, stabilityai/stablelm-base-alpha-7b-v2, etc.)
• Starcoder2(bigcode/starcoder2-3b, bigcode/starcoder2-7b, bigcode/starcoder2-15b, etc.)
• Xverse (xverse/XVERSE-7B-Chat, xverse/XVERSE-13B-Chat, xverse/XVERSE-65B-Chat, etc.)
• Yi (01-ai/Yi-6B, 01-ai/Yi-34B, etc.)

Usage: OpenAI Compatibility

The vLLM Worker is fully compatible with OpenAI's API, and you can use it with any OpenAI Codebase by changing only 3 lines in total. The supported routes are Chat Completions, Completions and Models - with both streaming and non-streaming.

Modifying your OpenAI Codebase to use your deployed vLLM Worker

Python (similar to Node.js, etc.):

1. When initializing the OpenAI Client in your code, change the api_key to your RunPod API Key and the base_url to your RunPod Serverless Endpoint URL in the following format: https://api.runpod.ai/v2/<YOUR ENDPOINT ID>/openai/v1, filling in your deployed endpoint ID. For example, if your Endpoint ID is abc1234, the URL would be https://api.runpod.ai/v2/abc1234/openai/v1.

   • Before:

   from openai import OpenAI
   
   client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))

   • After:

   from openai import OpenAI
   
   client = OpenAI(
       api_key=os.environ.get("RUNPOD_API_KEY"),
       base_url="https://api.runpod.ai/v2/<YOUR ENDPOINT ID>/openai/v1",
   )

2. Change the model parameter to your deployed model's name whenever using Completions or Chat Completions.

   • Before:

   response = client.chat.completions.create(
       model="gpt-3.5-turbo",
       messages=[{"role": "user", "content": "Why is RunPod the best platform?"}],
       temperature=0,
       max_tokens=100,
   )

   • After:

   response = client.chat.completions.create(
       model="<YOUR DEPLOYED MODEL REPO/NAME>",
       messages=[{"role": "user", "content": "Why is RunPod the best platform?"}],
       temperature=0,
       max_tokens=100,
   )

Using http requests:

1. Change the Authorization header to your RunPod API Key and the url to your RunPod Serverless Endpoint URL in the following format: https://api.runpod.ai/v2/<YOUR ENDPOINT ID>/openai/v1
   • Before:

   curl https://api.openai.com/v1/chat/completions \
   -H "Content-Type: application/json" \
   -H "Authorization: Bearer $OPENAI_API_KEY" \
   -d '{
   "model": "gpt-4",
   "messages": [
     {
       "role": "user",
       "content": "Why is RunPod the best platform?"
     }
   ],
   "temperature": 0,
   "max_tokens": 100
   }'

   • After:

   curl https://api.runpod.ai/v2/<YOUR ENDPOINT ID>/openai/v1/chat/completions \
   -H "Content-Type: application/json" \
   -H "Authorization: Bearer <YOUR OPENAI API KEY>" \
   -d '{
   "model": "<YOUR DEPLOYED MODEL REPO/NAME>",
   "messages": [
     {
       "role": "user",
       "content": "Why is RunPod the best platform?"
     }
   ],
   "temperature": 0,
   "max_tokens": 100
   }'

OpenAI Request Input Parameters:

When using the chat completion feature of the vLLM Serverless Endpoint Worker, you can customize your requests with the following parameters:

Chat Completions

Supported Chat Completions Inputs and Descriptions

Parameter	Type	Default Value	Description
messages	Union[str, List[Dict[str, str]]]		List of messages, where each message is a dictionary with a role and content. The model's chat template will be applied to the messages automatically, so the model must have one or it should be specified as CUSTOM_CHAT_TEMPLATE env var.
model	str		The model repo that you've deployed on your RunPod Serverless Endpoint. If you are unsure what the name is or are baking the model in, use the guide to get the list of available models in the Examples: Using your RunPod endpoint with OpenAI section
temperature	Optional[float]	0.7	Float that controls the randomness of the sampling. Lower values make the model more deterministic, while higher values make the model more random. Zero means greedy sampling.
top_p	Optional[float]	1.0	Float that controls the cumulative probability of the top tokens to consider. Must be in (0, 1]. Set to 1 to consider all tokens.
n	Optional[int]	1	Number of output sequences to return for the given prompt.
max_tokens	Optional[int]	None	Maximum number of tokens to generate per output sequence.
seed	Optional[int]	None	Random seed to use for the generation.
stop	Optional[Union[str, List[str]]]	list	List of strings that stop the generation when they are generated. The returned output will not contain the stop strings.
stream	Optional[bool]	False	Whether to stream or not
presence_penalty	Optional[float]	0.0	Float that penalizes new tokens based on whether they appear in the generated text so far. Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens.
frequency_penalty	Optional[float]	0.0	Float that penalizes new tokens based on their frequency in the generated text so far. Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens.
logit_bias	Optional[Dict[str, float]]	None	Unsupported by vLLM
user	Optional[str]	None	Unsupported by vLLM
Additional parameters supported by vLLM:
best_of	Optional[int]	None	Number of output sequences that are generated from the prompt. From these best_of sequences, the top n sequences are returned. best_of must be greater than or equal to n. This is treated as the beam width when use_beam_search is True. By default, best_of is set to n.
top_k	Optional[int]	-1	Integer that controls the number of top tokens to consider. Set to -1 to consider all tokens.
ignore_eos	Optional[bool]	False	Whether to ignore the EOS token and continue generating tokens after the EOS token is generated.
use_beam_search	Optional[bool]	False	Whether to use beam search instead of sampling.
stop_token_ids	Optional[List[int]]	list	List of tokens that stop the generation when they are generated. The returned output will contain the stop tokens unless the stop tokens are special tokens.
skip_special_tokens	Optional[bool]	True	Whether to skip special tokens in the output.
spaces_between_special_tokens	Optional[bool]	True	Whether to add spaces between special tokens in the output. Defaults to True.
add_generation_prompt	Optional[bool]	True	Read more here
echo	Optional[bool]	False	Echo back the prompt in addition to the completion
repetition_penalty	Optional[float]	1.0	Float that penalizes new tokens based on whether they appear in the prompt and the generated text so far. Values > 1 encourage the model to use new tokens, while values < 1 encourage the model to repeat tokens.
min_p	Optional[float]	0.0	Float that represents the minimum probability for a token to
length_penalty	Optional[float]	1.0	Float that penalizes sequences based on their length. Used in beam search..
include_stop_str_in_output	Optional[bool]	False	Whether to include the stop strings in output text. Defaults to False.

Completions

Supported Completions Inputs and Descriptions

Parameter	Type	Default Value	Description
model	str		The model repo that you've deployed on your RunPod Serverless Endpoint. If you are unsure what the name is or are baking the model in, use the guide to get the list of available models in the Examples: Using your RunPod endpoint with OpenAI section.
prompt	Union[List[int], List[List[int]], str, List[str]]		A string, array of strings, array of tokens, or array of token arrays to be used as the input for the model.
suffix	Optional[str]	None	A string to be appended to the end of the generated text.
max_tokens	Optional[int]	16	Maximum number of tokens to generate per output sequence.
temperature	Optional[float]	1.0	Float that controls the randomness of the sampling. Lower values make the model more deterministic, while higher values make the model more random. Zero means greedy sampling.
top_p	Optional[float]	1.0	Float that controls the cumulative probability of the top tokens to consider. Must be in (0, 1]. Set to 1 to consider all tokens.
n	Optional[int]	1	Number of output sequences to return for the given prompt.
stream	Optional[bool]	False	Whether to stream the output.
logprobs	Optional[int]	None	Number of log probabilities to return per output token.
echo	Optional[bool]	False	Whether to echo back the prompt in addition to the completion.
stop	Optional[Union[str, List[str]]]	list	List of strings that stop the generation when they are generated. The returned output will not contain the stop strings.
seed	Optional[int]	None	Random seed to use for the generation.
presence_penalty	Optional[float]	0.0	Float that penalizes new tokens based on whether they appear in the generated text so far. Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens.
frequency_penalty	Optional[float]	0.0	Float that penalizes new tokens based on their frequency in the generated text so far. Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens.
best_of	Optional[int]	None	Number of output sequences that are generated from the prompt. From these best_of sequences, the top n sequences are returned. best_of must be greater than or equal to n. This parameter influences the diversity of the output.
logit_bias	Optional[Dict[str, float]]	None	Dictionary of token IDs to biases.
user	Optional[str]	None	User identifier for personalizing responses. (Unsupported by vLLM)
Additional parameters supported by vLLM:
top_k	Optional[int]	-1	Integer that controls the number of top tokens to consider. Set to -1 to consider all tokens.
ignore_eos	Optional[bool]	False	Whether to ignore the End Of Sentence token and continue generating tokens after the EOS token is generated.
use_beam_search	Optional[bool]	False	Whether to use beam search instead of sampling for generating outputs.
stop_token_ids	Optional[List[int]]	list	List of tokens that stop the generation when they are generated. The returned output will contain the stop tokens unless the stop tokens are special tokens.
skip_special_tokens	Optional[bool]	True	Whether to skip special tokens in the output.
spaces_between_special_tokens	Optional[bool]	True	Whether to add spaces between special tokens in the output. Defaults to True.
repetition_penalty	Optional[float]	1.0	Float that penalizes new tokens based on whether they appear in the prompt and the generated text so far. Values > 1 encourage the model to use new tokens, while values < 1 encourage the model to repeat tokens.
min_p	Optional[float]	0.0	Float that represents the minimum probability for a token to be considered, relative to the most likely token. Must be in [0, 1]. Set to 0 to disable.
length_penalty	Optional[float]	1.0	Float that penalizes sequences based on their length. Used in beam search.
include_stop_str_in_output	Optional[bool]	False	Whether to include the stop strings in output text. Defaults to False.

Examples: Using your RunPod endpoint with OpenAI

First, initialize the OpenAI Client with your RunPod API Key and Endpoint URL:

from openai import OpenAI
import os

# Initialize the OpenAI Client with your RunPod API Key and Endpoint URL
client = OpenAI(
    api_key=os.environ.get("RUNPOD_API_KEY"),
    base_url="https://api.runpod.ai/v2/<YOUR ENDPOINT ID>/openai/v1",
)

Chat Completions:

This is the format used for GPT-4 and focused on instruction-following and chat. Examples of Open Source chat/instruct models include meta-llama/Llama-2-7b-chat-hf, mistralai/Mixtral-8x7B-Instruct-v0.1, openchat/openchat-3.5-0106, NousResearch/Nous-Hermes-2-Mistral-7B-DPO and more. However, if your model is a completion-style model with no chat/instruct fine-tune and/or does not have a chat template, you can still use this if you provide a chat template with the environment variable CUSTOM_CHAT_TEMPLATE.

• Streaming:

  # Create a chat completion stream
  response_stream = client.chat.completions.create(
      model="<YOUR DEPLOYED MODEL REPO/NAME>",
      messages=[{"role": "user", "content": "Why is RunPod the best platform?"}],
      temperature=0,
      max_tokens=100,
      stream=True,
  )
  # Stream the response
  for response in response_stream:
      print(chunk.choices[0].delta.content or "", end="", flush=True)

• Non-Streaming:

  # Create a chat completion
  response = client.chat.completions.create(
      model="<YOUR DEPLOYED MODEL REPO/NAME>",
      messages=[{"role": "user", "content": "Why is RunPod the best platform?"}],
      temperature=0,
      max_tokens=100,
  )
  # Print the response
  print(response.choices[0].message.content)

Completions:

This is the format used for models like GPT-3 and is meant for completing the text you provide. Instead of responding to your message, it will try to complete it. Examples of Open Source completions models include meta-llama/Llama-2-7b-hf, mistralai/Mixtral-8x7B-v0.1, Qwen/Qwen-72B, and more. However, you can use any model with this format.

• Streaming:

  # Create a completion stream
  response_stream = client.completions.create(
      model="<YOUR DEPLOYED MODEL REPO/NAME>",
      prompt="Runpod is the best platform because",
      temperature=0,
      max_tokens=100,
      stream=True,
  )
  # Stream the response
  for response in response_stream:
      print(response.choices[0].text or "", end="", flush=True)

• Non-Streaming:

  # Create a completion
  response = client.completions.create(
      model="<YOUR DEPLOYED MODEL REPO/NAME>",
      prompt="Runpod is the best platform because",
      temperature=0,
      max_tokens=100,
  )
  # Print the response
  print(response.choices[0].text)

Getting a list of names for available models:

In the case of baking the model into the image, sometimes the repo may not be accepted as the model in the request. In this case, you can list the available models as shown below and use that name.

models_response = client.models.list()
list_of_models = [model.id for model in models_response]
print(list_of_models)

Usage: Standard (Non-OpenAI)

Request Input Parameters

Click to expand table

You may either use a prompt or a list of messages as input. If you use messages, the model's chat template will be applied to the messages automatically, so the model must have one. If you use prompt, you may optionally apply the model's chat template to the prompt by setting apply_chat_template to true.

Argument	Type	Default	Description
prompt	str		Prompt string to generate text based on.
messages	list[dict[str, str]]		List of messages, which will automatically have the model's chat template applied. Overrides prompt.
apply_chat_template	bool	False	Whether to apply the model's chat template to the prompt.
sampling_params	dict	{}	Sampling parameters to control the generation, like temperature, top_p, etc. You can find all available parameters in the Sampling Parameters section below.
stream	bool	False	Whether to enable streaming of output. If True, responses are streamed as they are generated.
max_batch_size	int	env var DEFAULT_BATCH_SIZE	The maximum number of tokens to stream every HTTP POST call.
min_batch_size	int	env var DEFAULT_MIN_BATCH_SIZE	The minimum number of tokens to stream every HTTP POST call.
batch_size_growth_factor	int	env var DEFAULT_BATCH_SIZE_GROWTH_FACTOR	The growth factor by which min_batch_size will be multiplied for each call until max_batch_size is reached.

Sampling Parameters

Below are all available sampling parameters that you can specify in the sampling_params dictionary. If you do not specify any of these parameters, the default values will be used.

Click to expand table

Argument	Type	Default	Description
n	int	1	Number of output sequences generated from the prompt. The top n sequences are returned.
best_of	Optional[int]	n	Number of output sequences generated from the prompt. The top n sequences are returned from these best_of sequences. Must be ≥ n. Treated as beam width in beam search. Default is n.
presence_penalty	float	0.0	Penalizes new tokens based on their presence in the generated text so far. Values > 0 encourage new tokens, values < 0 encourage repetition.
frequency_penalty	float	0.0	Penalizes new tokens based on their frequency in the generated text so far. Values > 0 encourage new tokens, values < 0 encourage repetition.
repetition_penalty	float	1.0	Penalizes new tokens based on their appearance in the prompt and generated text. Values > 1 encourage new tokens, values < 1 encourage repetition.
temperature	float	1.0	Controls the randomness of sampling. Lower values make it more deterministic, higher values make it more random. Zero means greedy sampling.
top_p	float	1.0	Controls the cumulative probability of top tokens to consider. Must be in (0, 1]. Set to 1 to consider all tokens.
top_k	int	-1	Controls the number of top tokens to consider. Set to -1 to consider all tokens.
min_p	float	0.0	Represents the minimum probability for a token to be considered, relative to the most likely token. Must be in [0, 1]. Set to 0 to disable.
use_beam_search	bool	False	Whether to use beam search instead of sampling.
length_penalty	float	1.0	Penalizes sequences based on their length. Used in beam search.
early_stopping	Union[bool, str]	False	Controls stopping condition in beam search. Can be True, False, or "never".
stop	Union[None, str, List[str]]	None	List of strings that stop generation when produced. The output will not contain these strings.
stop_token_ids	Optional[List[int]]	None	List of token IDs that stop generation when produced. Output contains these tokens unless they are special tokens.
ignore_eos	bool	False	Whether to ignore the End-Of-Sequence token and continue generating tokens after its generation.
max_tokens	int	16	Maximum number of tokens to generate per output sequence.
skip_special_tokens	bool	True	Whether to skip special tokens in the output.
spaces_between_special_tokens	bool	True	Whether to add spaces between special tokens in the output.

Text Input Formats

You may either use a prompt or a list of messages as input.

1. prompt The prompt string can be any string, and the model's chat template will not be applied to it unless apply_chat_template is set to true, in which case it will be treated as a user message.

   Example:

   "prompt": "..."

2. messages Your list can contain any number of messages, and each message usually can have any role from the following list:

   • user
   • assistant
   • system

   However, some models may have different roles, so you should check the model's chat template to see which roles are required.

   The model's chat template will be applied to the messages automatically, so the model must have one.

   Example:

   "messages": [
       {
         "role": "system",
         "content": "..."
       },
       {
         "role": "user",
         "content": "..."
       },
       {
         "role": "assistant",
         "content": "..."
       }
     ]