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Added list of exported metrics #207

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70 changes: 56 additions & 14 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -26,33 +26,32 @@ LoRAX (LoRA eXchange) is a framework that allows users to serve thousands of fin
- [Prompt via REST API](#prompt-via-rest-api)
- [Prompt via Python Client](#prompt-via-python-client)
- [Chat via OpenAI API](#chat-via-openai-api)
- [Metrics](#metrics)
- [Next steps](#next-steps)
- [🙇 Acknowledgements](#-acknowledgements)
- [🗺️ Roadmap](#️-roadmap)

## 🌳 Features

- 🚅 **Dynamic Adapter Loading:** include any fine-tuned LoRA adapter in your request, it will be loaded just-in-time without blocking concurrent requests.
- 🏋️‍♀️ **Heterogeneous Continuous Batching:** packs requests for different adapters together into the same batch, keeping latency and throughput nearly constant with the number of concurrent adapters.
- 🧁 **Adapter Exchange Scheduling:** asynchronously prefetches and offloads adapters between GPU and CPU memory, schedules request batching to optimize the aggregate throughput of the system.
- 👬 **Optimized Inference:** high throughput and low latency optimizations including tensor parallelism, pre-compiled CUDA kernels ([flash-attention](https://arxiv.org/abs/2307.08691), [paged attention](https://arxiv.org/abs/2309.06180), [SGMV](https://arxiv.org/abs/2310.18547)), quantization, token streaming.
- 🚢 **Ready for Production** prebuilt Docker images, Helm charts for Kubernetes, Prometheus metrics, and distributed tracing with Open Telemetry. OpenAI compatible API supporting multi-turn chat conversations. Private adapters through per-request tenant isolation.
- 🤯 **Free for Commercial Use:** Apache 2.0 License. Enough said 😎.

- 🚅 **Dynamic Adapter Loading:** include any fine-tuned LoRA adapter in your request, it will be loaded just-in-time without blocking concurrent requests.
- 🏋️‍♀️ **Heterogeneous Continuous Batching:** packs requests for different adapters together into the same batch, keeping latency and throughput nearly constant with the number of concurrent adapters.
- 🧁 **Adapter Exchange Scheduling:** asynchronously prefetches and offloads adapters between GPU and CPU memory, schedules request batching to optimize the aggregate throughput of the system.
- 👬 **Optimized Inference:** high throughput and low latency optimizations including tensor parallelism, pre-compiled CUDA kernels ([flash-attention](https://arxiv.org/abs/2307.08691), [paged attention](https://arxiv.org/abs/2309.06180), [SGMV](https://arxiv.org/abs/2310.18547)), quantization, token streaming.
- 🚢 **Ready for Production** prebuilt Docker images, Helm charts for Kubernetes, Prometheus metrics, and distributed tracing with Open Telemetry. OpenAI compatible API supporting multi-turn chat conversations. Private adapters through per-request tenant isolation.
- 🤯 **Free for Commercial Use:** Apache 2.0 License. Enough said 😎.

<p align="center">
<img src="https://github.com/predibase/lorax/assets/29719151/f88aa16c-66de-45ad-ad40-01a7874ed8a9" />
</p>


## 🏠 Models

Serving a fine-tuned model with LoRAX consists of two components:

- [Base Model](https://predibase.github.io/lorax/models/base_models): pretrained large model shared across all adapters.
- [Adapter](https://predibase.github.io/lorax/models/adapters): task-specific adapter weights dynamically loaded per request.
- [Base Model](https://predibase.github.io/lorax/models/base_models): pretrained large model shared across all adapters.
- [Adapter](https://predibase.github.io/lorax/models/adapters): task-specific adapter weights dynamically loaded per request.

LoRAX supports a number of Large Language Models as the base model including [Llama](https://huggingface.co/meta-llama) (including [CodeLlama](https://huggingface.co/codellama)), [Mistral](https://huggingface.co/mistralai) (including [Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)), and [Qwen](https://huggingface.co/Qwen). See [Supported Architectures](https://predibase.github.io/lorax/models/base_models/#supported-architectures) for a complete list of supported base models.
LoRAX supports a number of Large Language Models as the base model including [Llama](https://huggingface.co/meta-llama) (including [CodeLlama](https://huggingface.co/codellama)), [Mistral](https://huggingface.co/mistralai) (including [Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)), and [Qwen](https://huggingface.co/Qwen). See [Supported Architectures](https://predibase.github.io/lorax/models/base_models/#supported-architectures) for a complete list of supported base models.

Base models can be loaded in fp16 or quantized with `bitsandbytes`, [GPT-Q](https://arxiv.org/abs/2210.17323), or [AWQ](https://arxiv.org/abs/2306.00978).

Expand Down Expand Up @@ -163,13 +162,56 @@ print("Response:", resp[0].choices[0].message.content)

See [OpenAI Compatible API](https://predibase.github.io/lorax/guides/openai_api) for details.

### Metrics
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Can you add this to docs instead? We have a section called Reference, we could add a page called Prometheus Metrics or similar.

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Also, is this something we can expose via the openapi.json?


LoRAX exports metrics in the standard Prometheus format. Metrics are available via a `GET` request to the `/metrics` endpoint of the server.

| Metric |
| ------------------------------------------------- |
| lorax_batch_current_max_tokens |
| lorax_batch_current_size |
| lorax_batch_inference_count |
| lorax_batch_inference_duration_bucket |
| lorax_batch_inference_duration_count |
| lorax_batch_inference_duration_sum |
| lorax_batch_inference_success |
| lorax_batch_next_size_bucket |
| lorax_batch_next_size_count |
| lorax_batch_next_size_sum |
| lorax_request_count |
| lorax_request_duration_bucket |
| lorax_request_duration_count |
| lorax_request_duration_sum |
| lorax_request_generated_tokens_bucket |
| lorax_request_generated_tokens_count |
| lorax_request_generated_tokens_sum |
| lorax_request_inference_duration_bucket |
| lorax_request_inference_duration_count |
| lorax_request_inference_duration_sum |
| lorax_request_input_length_bucket |
| lorax_request_input_length_count |
| lorax_request_input_length_sum |
| lorax_request_max_new_tokens_bucket |
| lorax_request_max_new_tokens_count |
| lorax_request_max_new_tokens_sum |
| lorax_request_mean_time_per_token_duration_bucket |
| lorax_request_mean_time_per_token_duration_count |
| lorax_request_mean_time_per_token_duration_sum |
| lorax_request_queue_duration_bucket |
| lorax_request_queue_duration_count |
| lorax_request_queue_duration_sum |
| lorax_request_success |
| lorax_request_validation_duration_bucket |
| lorax_request_validation_duration_count |
| lorax_request_validation_duration_sum |

### Next steps

Here are some other interesting Mistral-7B fine-tuned models to try out:

- [alignment-handbook/zephyr-7b-dpo-lora](https://huggingface.co/alignment-handbook/zephyr-7b-dpo-lora): Mistral-7b fine-tuned on Zephyr-7B dataset with DPO.
- [IlyaGusev/saiga_mistral_7b_lora](https://huggingface.co/IlyaGusev/saiga_mistral_7b_lora): Russian chatbot based on `Open-Orca/Mistral-7B-OpenOrca`.
- [Undi95/Mistral-7B-roleplay_alpaca-lora](https://huggingface.co/Undi95/Mistral-7B-roleplay_alpaca-lora): Fine-tuned using role-play prompts.
- [alignment-handbook/zephyr-7b-dpo-lora](https://huggingface.co/alignment-handbook/zephyr-7b-dpo-lora): Mistral-7b fine-tuned on Zephyr-7B dataset with DPO.
- [IlyaGusev/saiga_mistral_7b_lora](https://huggingface.co/IlyaGusev/saiga_mistral_7b_lora): Russian chatbot based on `Open-Orca/Mistral-7B-OpenOrca`.
- [Undi95/Mistral-7B-roleplay_alpaca-lora](https://huggingface.co/Undi95/Mistral-7B-roleplay_alpaca-lora): Fine-tuned using role-play prompts.

You can find more LoRA adapters [here](https://huggingface.co/models?pipeline_tag=text-generation&sort=trending&search=-lora), or try fine-tuning your own with [PEFT](https://github.com/huggingface/peft) or [Ludwig](https://ludwig.ai).

Expand Down