worker-vllm/README.md

vLLM Serverless Endpoint Worker

Deploy Blazing-fast LLMs powered by vLLM on RunPod Serverless in a few clicks.

Worker vLLM 0.3.0: What's New since 0.2.0:

• 🚀 Full OpenAI Compatibility 🚀

  You may now use your deployment 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.

• Dynamic Batch Size - time-to-first token as fast no batching, while maintaining the performance of batched token streaming throughout the request.

• Custom chat templates that you can specify as an environment variable.

• Fixed Tensor Parallelism, baking model into images, and more bugs.

Table of Contents

• Setting up the Serverless Worker
  • Option 1: Deploy Any Model Using Pre-Built Docker Image [Recommended]
    • Prerequisites
    • Environment Variables
  • 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]

Tip

This is the recommended way to deploy your model, as it does not require you to build a Docker image, upload heavy models to DockerHub and wait for workers to download them. Instead, use this option to deploy your model in a few clicks. For even more convenience, attach a network storage volume to your Endpoint, which will download the model once and share it across all workers.

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:

Stable Image: runpod/worker-vllm:0.2.3

Development Image: runpod/worker-vllm:dev

Prerequisites

• RunPod Account

Environment Variables

Required:

• MODEL_NAME: Hugging Face Model Repository (e.g., openchat/openchat-3.5-1210).

Optional:

• LLM Settings:

  • MODEL_REVISION: Model revision to load (default: None).
  • MAX_MODEL_LENGTH: Maximum number of tokens for the engine to be able to handle. (default: maximum supported by the model)
  • 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)
  • LOAD_FORMAT: Format to load model in (default: auto).
  • HF_TOKEN: Hugging Face token for private and gated models (e.g., Llama, Falcon).
  • QUANTIZATION: AWQ (awq), SqueezeLLM (squeezellm) or GPTQ (gptq) Quantization. The specified Model Repo must be of a quantized model. (default: None)
  • TRUST_REMOTE_CODE: Trust remote code for Hugging Face (default: 0)

• Tokenizer Settings:

  • 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: None).
  • CUSTOM_CHAT_TEMPLATE: Custom chat jinja template, read more about Hugging Face chat templates here. (default: None)

• Tensor Parallelism: Note that the more GPUs you split a model's weights across, the slower it will be due to inter-GPU communication overhead. If you can fit the model on a single GPU, it is recommended to do so.

  • TENSOR_PARALLEL_SIZE: Number of GPUs to shard the model across (default: 1).
  • If you are having issues loading your model with Tensor Parallelism, try decreasing VLLM_CPU_FRACTION (default: 1).

• System Settings:

  • GPU_MEMORY_UTILIZATION: GPU VRAM utilization (default: 0.98).
  • MAX_PARALLEL_LOADING_WORKERS: Maximum number of parallel workers for loading models, for non-Tensor Parallel only. (default: number of available CPU cores if TENSOR_PARALLEL_SIZE is 1, otherwise None).

• Streaming Batch Size:

  • DEFAULT_BATCH_SIZE: Token streaming batch size (default: 50). This reduces the number of HTTP calls, increasing speed 8-10x vs non-batching, matching non-streaming performance.

  • Dynamic Batch Size:

    • DEFAULT_MIN_BATCH_SIZE: the batch size for the first request (default: 1).
    • DEFAULT_BATCH_SIZE_GROWTH_FACTOR: the growth factor for the dynamic batch size (default: 3).

    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.

• OpenAI Settings:

  • RAW_OPENAI_OUTPUT: Enable (1) or disable (0) raw OpenAI SSE format string output when streaming(default: 1). Required to be enabled (default) for OpenAI compatibility.

• Serverless Settings:

  • MAX_CONCURRENCY: Max concurrent requests. (default: 100)
  • DISABLE_LOG_STATS: Enable (0) or disable (1) vLLM stats logging.
  • DISABLE_LOG_REQUESTS: Enable (0) or disable (1) request logging.

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

• 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, etc.)
• Phi (microsoft/phi-1_5, microsoft/phi-2, etc.)
• LLaMA & LLaMA-2 (meta-llama/Llama-2-70b-hf, lmsys/vicuna-13b-v1.3, young-geng/koala, openlm-research/open_llama_13b, etc.)
• Qwen2 (Qwen/Qwen2-7B-beta, Qwen/Qwen-7B-Chat-beta, etc.)
• StableLM(stabilityai/stablelm-3b-4e1t, stabilityai/stablelm-base-alpha-7b-v2, etc.)
• Yi (01-ai/Yi-6B, 01-ai/Yi-34B, etc.)
• Qwen (Qwen/Qwen-7B, Qwen/Qwen-7B-Chat, etc.)
• 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.)
• DeciLM (Deci/DeciLM-7B, Deci/DeciLM-7B-instruct, etc.)
• Falcon (tiiuae/falcon-7b, tiiuae/falcon-40b, tiiuae/falcon-rw-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.)
• MPT (mosaicml/mpt-7b, mosaicml/mpt-30b, etc.)
• OPT (facebook/opt-66b, facebook/opt-iml-max-30b, 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.

   • Before:

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

   • After:

   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

Click to expand table

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

Click to expand table

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)

Non-OpenAI Usage

Input Request 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.
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.

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": "..."
       }
     ]
   

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.