Skip to content
/ worker-vllm Public
forked from runpod-workers/worker-vllm

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

License

MIT license
0 stars 99 forks Branches Tags Activity
Star
Notifications You must be signed in to change notification settings

jacobcwright/worker-vllm

BranchesTags
 
 

Repository files navigation

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 v1.4.0 with vLLM 0.6.1 now available under stable tags

Update v1.3.1 is now available, use the image tag runpod/worker-v1-vllm:v1.4.0stable-cuda12.1.0.

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
12.1.0 runpod/worker-v1-vllm:v1.4.0stable-cuda12.1.0 runpod/worker-v1-vllm:v1.4.0dev-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 as int values.

Name Default Type/Choices Description
MODEL_NAME 'facebook/opt-125m' str Name or path of the Hugging Face model to use.
TOKENIZER None str Name or path of the Hugging Face tokenizer to use.
SKIP_TOKENIZER_INIT False bool Skip initialization of tokenizer and detokenizer.
TOKENIZER_MODE 'auto' ['auto', 'slow'] The tokenizer mode.
TRUST_REMOTE_CODE False bool Trust remote code from Hugging Face.
DOWNLOAD_DIR None str Directory to download and load the weights.
LOAD_FORMAT 'auto' str The format of the model weights to load.
HF_TOKEN - str Hugging Face token for private and gated models.
DTYPE 'auto' ['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'] Data type for model weights and activations.
KV_CACHE_DTYPE 'auto' ['auto', 'fp8'] Data type for KV cache storage.
QUANTIZATION_PARAM_PATH None str Path to the JSON file containing the KV cache scaling factors.
MAX_MODEL_LEN None int Model context length.
GUIDED_DECODING_BACKEND 'outlines' ['outlines', 'lm-format-enforcer'] Which engine will be used for guided decoding by default.
DISTRIBUTED_EXECUTOR_BACKEND None ['ray', 'mp'] Backend to use for distributed serving.
WORKER_USE_RAY False bool Deprecated, use --distributed-executor-backend=ray.
PIPELINE_PARALLEL_SIZE 1 int Number of pipeline stages.
TENSOR_PARALLEL_SIZE 1 int Number of tensor parallel replicas.
MAX_PARALLEL_LOADING_WORKERS None int Load model sequentially in multiple batches.
RAY_WORKERS_USE_NSIGHT False bool If specified, use nsight to profile Ray workers.
ENABLE_PREFIX_CACHING False bool Enables automatic prefix caching.
DISABLE_SLIDING_WINDOW False bool Disables sliding window, capping to sliding window size.
USE_V2_BLOCK_MANAGER False bool Use BlockSpaceMangerV2.
NUM_LOOKAHEAD_SLOTS 0 int Experimental scheduling config necessary for speculative decoding.
SEED 0 int Random seed for operations.
NUM_GPU_BLOCKS_OVERRIDE None int If specified, ignore GPU profiling result and use this number of GPU blocks.
MAX_NUM_BATCHED_TOKENS None int Maximum number of batched tokens per iteration.
MAX_NUM_SEQS 256 int Maximum number of sequences per iteration.
MAX_LOGPROBS 20 int Max number of log probs to return when logprobs is specified in SamplingParams.
DISABLE_LOG_STATS False bool Disable logging statistics.
QUANTIZATION None ['awq', 'squeezellm', 'gptq'] Method used to quantize the weights.
ROPE_SCALING None dict RoPE scaling configuration in JSON format.
ROPE_THETA None float RoPE theta. Use with rope_scaling.
TOKENIZER_POOL_SIZE 0 int Size of tokenizer pool to use for asynchronous tokenization.
TOKENIZER_POOL_TYPE 'ray' str Type of tokenizer pool to use for asynchronous tokenization.
TOKENIZER_POOL_EXTRA_CONFIG None dict Extra config for tokenizer pool.
ENABLE_LORA False bool If True, enable handling of LoRA adapters.
MAX_LORAS 1 int Max number of LoRAs in a single batch.
MAX_LORA_RANK 16 int Max LoRA rank.
LORA_EXTRA_VOCAB_SIZE 256 int Maximum size of extra vocabulary for LoRA adapters.
LORA_DTYPE 'auto' ['auto', 'float16', 'bfloat16', 'float32'] Data type for LoRA.
LONG_LORA_SCALING_FACTORS None tuple Specify multiple scaling factors for LoRA adapters.
MAX_CPU_LORAS None int Maximum number of LoRAs to store in CPU memory.
FULLY_SHARDED_LORAS False bool Enable fully sharded LoRA layers.
SCHEDULER_DELAY_FACTOR 0.0 float Apply a delay before scheduling next prompt.
ENABLE_CHUNKED_PREFILL False bool Enable chunked prefill requests.
SPECULATIVE_MODEL None str The name of the draft model to be used in speculative decoding.
NUM_SPECULATIVE_TOKENS None int The number of speculative tokens to sample from the draft model.
SPECULATIVE_DRAFT_TENSOR_PARALLEL_SIZE None int Number of tensor parallel replicas for the draft model.
SPECULATIVE_MAX_MODEL_LEN None int The maximum sequence length supported by the draft model.
SPECULATIVE_DISABLE_BY_BATCH_SIZE None int Disable speculative decoding if the number of enqueue requests is larger than this value.
NGRAM_PROMPT_LOOKUP_MAX None int Max size of window for ngram prompt lookup in speculative decoding.
NGRAM_PROMPT_LOOKUP_MIN None int Min size of window for ngram prompt lookup in speculative decoding.
SPEC_DECODING_ACCEPTANCE_METHOD 'rejection_sampler' ['rejection_sampler', 'typical_acceptance_sampler'] Specify the acceptance method for draft token verification in speculative decoding.
TYPICAL_ACCEPTANCE_SAMPLER_POSTERIOR_THRESHOLD None float Set the lower bound threshold for the posterior probability of a token to be accepted.
TYPICAL_ACCEPTANCE_SAMPLER_POSTERIOR_ALPHA None float A scaling factor for the entropy-based threshold for token acceptance.
MODEL_LOADER_EXTRA_CONFIG None dict Extra config for model loader.
PREEMPTION_MODE None str If 'recompute', the engine performs preemption-aware recomputation. If 'save', the engine saves activations into the CPU memory as preemption happens.
PREEMPTION_CHECK_PERIOD 1.0 float How frequently the engine checks if a preemption happens.
PREEMPTION_CPU_CAPACITY 2 float The percentage of CPU memory used for the saved activations.
DISABLE_LOGGING_REQUEST False bool Disable logging requests.
MAX_LOG_LEN None int Max number of prompt characters or prompt ID numbers being printed in log.
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 False bool 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 False bool Enables or disables vLLM stats logging.
DISABLE_LOG_REQUESTS False bool 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: 12.1.0 (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 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 [RECOMMENDED]

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.

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)

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": "..."
    }
  ]

Worker Config

The worker config is a JSON file that is used to build the form that helps users configure their serverless endpoint on the RunPod Web Interface.

Note: This is a new feature and only works for workers that use one model

Writing your worker-config.json

The JSON consists of two main parts, schema and versions.

  • schema: Here you specify the form fields that will be displayed to the user.
    • env_var_name: The name of the environment variable that is being set using the form field.
    • value: This is the default value of the form field. It will be shown in the UI as such unless the user changes it.
    • title: This is the title of the form field in the UI.
    • description: This is the description of the form field in the UI.
    • required: This is a boolean that specifies if the form field is required.
    • type: This is the type of the form field. Options are:
      • text: Environment variable is a string so user inputs text in form field.
      • select: User selects one option from the dropdown. You must provide the options key value pair after type if using this.
      • toggle: User toggles between true and false.
      • number: User inputs a number in the form field.
    • options: Specify the options the user can select from if the type is select. DO NOT include this unless the type is select.
  • versions: This is where you call the form fields specified in schema and organize them into categories.
    • imageName: This is the name of the Docker image that will be used to run the serverless endpoint.
    • minimumCudaVersion: This is the minimum CUDA version that is required to run the serverless endpoint.
    • categories: This is where you call the keys of the form fields specified in schema and organize them into categories. Each category is a toggle list of forms on the Web UI.
      • title: This is the title of the category in the UI.
      • settings: This is the array of settings schemas specified in schema associated with the category.

Example of schema

{
  "schema": {
    "TOKENIZER": {
      "env_var_name": "TOKENIZER",
      "value": "",
      "title": "Tokenizer",
      "description": "Name or path of the Hugging Face tokenizer to use.",
      "required": false,
      "type": "text"
    }, 
    "TOKENIZER_MODE": {
      "env_var_name": "TOKENIZER_MODE",
      "value": "auto",
      "title": "Tokenizer Mode",
      "description": "The tokenizer mode.",
      "required": false,
      "type": "select",
      "options": [
        { "value": "auto", "label": "auto" },
        { "value": "slow", "label": "slow" }
      ]
    },
    ...
  }
}

Example of versions

{
  "versions": {
    "0.5.4": {
      "imageName": "runpod/worker-v1-vllm:v1.2.0stable-cuda12.1.0",
      "minimumCudaVersion": "12.1",
      "categories": [
        {
          "title": "LLM Settings",
          "settings": [
            "TOKENIZER", "TOKENIZER_MODE", "OTHER_SETTINGS_SCHEMA_KEYS_YOU_HAVE_SPECIFIED_0", ...
          ]
        },
        {
          "title": "Tokenizer Settings",
          "settings": [
            "OTHER_SETTINGS_SCHEMA_KEYS_0", "OTHER_SETTINGS_SCHEMA_KEYS_1", ...
          ]
        },
        ...
      ]
    }
  }
}

About

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

Resources

Readme

License

MIT license
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

No releases published

Packages

No packages published

Languages

  • Python 92.9%
  • Dockerfile 5.4%
  • HCL 1.7%