/worker-vllm

The RunPod worker template for serving our large language model endpoints. Powered by vLLM.

Primary LanguagePythonMIT LicenseMIT

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:

Demo of Deploying vLLM Worker on RunPod console with new UI

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

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

3. OpenAI-Compatible Embedding Worker Released

Deploy your own OpenAI-compatible Serverless Endpoint on RunPod with multiple embedding models and fast inference for RAG and more!

4. 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]

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 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_SEQ_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
  1. Export your Hugging Face token as an environment variable
export HF_TOKEN="your_token_here"
  1. 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": "..."
    }
  ]