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Changelog

We are following semantic versioning with strict backward-compatibility policy.

You can find out backwards-compatibility policy here.

Changes for the upcoming release can be found in the 'changelog.d' directory in our repository.

0.2.13

No significant changes.

0.2.12

Features

  • Added support for base container with OpenLLM. The base container will contains all necessary requirements to run OpenLLM. Currently it does included compiled version of FlashAttention v2, vLLM, AutoGPTQ and triton.

    This will now be the base image for all future BentoLLM. The image will also be published to public GHCR.

    To extend and use this image into your bento, simply specify base_image under bentofile.yaml:

    docker:
  base_image: ghcr.io/bentoml/openllm:<hash>

    The release strategy would include:

    • versioning of ghcr.io/bentoml/openllm:sha-<sha1> for every commit to main, ghcr.io/bentoml/openllm:0.2.11 for specific release version
    • alias latest will be managed with docker/build-push-action (discouraged)

    Note that all these images include compiled kernels that has been tested on Ampere GPUs with CUDA 11.8.

    To quickly run the image, do the following:

    docker run --rm --gpus all -it -v /home/ubuntu/.local/share/bentoml:/tmp/bentoml -e BENTOML_HOME=/tmp/bentoml \
            -e OPENLLM_USE_LOCAL_LATEST=True -e OPENLLM_LLAMA_FRAMEWORK=vllm ghcr.io/bentoml/openllm:2b5e96f90ad314f54e07b5b31e386e7d688d9bb2 start llama --model-id meta-llama/Llama-2-7b-chat-hf --workers-per-resource conserved --debug`

    In conjunction with this, OpenLLM now also have a set of small CLI utilities via openllm ext for ease-of-use

    General fixes around codebase bytecode optimization

    Fixes logs output to filter correct level based on --debug and --quiet

    openllm build now will default run model check locally. To skip it pass in --fast (before this is the default behaviour, but --no-fast as default makes more sense here as openllm build should also be able to run standalone)

    All LlaMA namespace has been renamed to Llama (internal change and shouldn't affect end users)

    openllm.AutoModel.for_model now will always return the instance. Runner kwargs will be handled via create_runner #142

  • All OpenLLM base container now are scanned for security vulnerabilities using trivy (both SBOM mode and CVE) #169

0.2.11

Features

  • Added embeddings support for T5 and ChatGLM #153

0.2.10

Features

  • Added installing with git-archival support

    pip install "https://github.com/bentoml/openllm/archive/main.tar.gz"

    #143

  • Users now can call client.embed to get the embeddings from the running LLMServer

    ```python
client = openllm.client.HTTPClient("http://localhost:3000")

client.embed("Hello World")
client.embed(["Hello", "World"])
```

    Note: The client.embed is currently only implemnted for openllm.client.HTTPClient and openllm.client.AsyncHTTPClient

    Users can also query embeddings directly from the CLI, via openllm embed:

    ```bash
$ openllm embed --endpoint localhost:3000 "Hello World" "My name is Susan"

[[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]
```

    #146

Bug fix

  • Fixes model location while running within BentoContainer correctly

    This makes sure that the tags and model path are inferred correctly, based on BENTO_PATH and /.dockerenv #141

0.2.9

No significant changes.

0.2.8

Features

  • APIs for LLMService are now provisional based on the capabilities of the LLM.

    The following APIs are considered provisional:

    • /v1/embeddings: This will be available if the LLM supports embeddings (i.e: LLM.embeddings is implemented. Example model are llama)
    • /hf/agent: This will be available if LLM supports running HF agents (i.e: LLM.generate_one is implemented. Example model are starcoder, falcon.)
    • POST /v1/adapters and GET /v1/adapters: This will be available if the server is running with LoRA weights

    openllm.LLMRunner now include three additional boolean:

    • runner.supports_embeddings: Whether this runner supports embeddings
    • runner.supports_hf_agent: Whether this runner support HF agents
    • runner.has_adapters: Whether this runner is loaded with LoRA adapters.

    Optimized openllm.models's bytecode performance #133

0.2.7

No significant changes.

0.2.6

Backwards-incompatible Changes

  • Updated signature for load_model and load_tokenizer not to allow tag. Tag can be accessed via llm.tag, or if using openllm.serialisation or bentoml.transformers then you can use self._bentomodel

    Updated serialisation shared logics to reduce callstack for saving three calltrace. #132

0.2.5

Features

  • Added support for sending arguments via CLI.

    openllm query --endpoint localhost:3000 "What is the difference between noun and pronoun?" --sampling-params temperature 0.84

    Fixed llama2 qlora training script to save unquantized weights #130

0.2.4

No significant changes.

0.2.3

No significant changes.

0.2.2

No significant changes.

0.2.1

No significant changes.

0.2.0

Features

  • Added support for GPTNeoX models. All variants of GPTNeoX, including Dolly-V2 and StableLM can now also use openllm start gpt-neox

    openllm models -o json nows return CPU and GPU field. openllm models now show table that mimics the one from README.md

    Added scripts to automatically add models import to __init__.py

    --workers-per-resource now accepts the following strategies:

    • round_robin: Similar behaviour when setting --workers-per-resource 1. This is useful for smaller models.
    • conserved: This will determine the number of available GPU resources, and only assign one worker for the LLMRunner with all available GPU resources. For example, if ther are 4 GPUs available, then conserved is equivalent to --workers-per-resource 0.25. #106

  • Added support for Baichuan model generation, contributed by @hetaoBackend

    Fixes how we handle model loader auto class for trust_remote_code in transformers #115

Bug fix

  • Fixes relative model_id handling for running LLM within the container.

    Added support for building container directly with openllm build. Users now can do openllm build --format=container:

    openllm build flan-t5 --format=container

    This is equivalent to:

    openllm build flan-t5 && bentoml containerize google-flan-t5-large-service

    Added Snapshot testing and more robust edge cases for model testing

    General improvement in openllm.LLM.import_model where it will parse santised parameters automatically.

    Fixes openllm start <bento> to use correct model_id, ignoring --model-id (The correct behaviour)

    Fixes --workers-per-resource conserved to respect --device

    Added initial interface for LLM.embeddings #107

  • Fixes resources to correctly follows CUDA_VISIBLE_DEVICES spec

    OpenLLM now contains a standalone parser that mimic torch.cuda parser for set GPU devices. This parser will be used to parse both AMD and NVIDIA GPUs.

    openllm should now be able to parse GPU- and MIG- UUID from both configuration or spec. #114

0.1.20

Features

  • Fine-tuning support for Falcon

    Added support for fine-tuning Falcon models with QLoRa

    OpenLLM now brings a openllm playground, which create a jupyter notebook for easy fine-tuning script

    Currently, it supports fine-tuning OPT and Falcon, more to come.

    openllm.LLM now provides a prepare_for_training helpers to easily setup LoRA and related configuration for fine-tuning #98

Bug fix

  • Fixes loading MPT config on CPU

    Fixes runner StopIteration on GET for Starlette App #92

  • openllm.LLM now generates tags based on given model_id and optional model_version.

    If given model_id is a custom path, the name would be the basename of the directory, and version would be the hash of the last modified time.

    openllm start now provides a --runtime, allowing setup different runtime. Currently it refactors to transformers. GGML is working in progress.

    Fixes miscellaneous items when saving models with quantized weights. #102

0.1.19

No significant changes.

0.1.18

Features

  • openllm.LLMConfig now supports dict() protocol

    config = openllm.LLMConfig.for_model("opt")

print(config.items())
print(config.values())
print(config.keys())
print(dict(config))

    #85

  • Added supports for MPT to OpenLLM

    Fixes a LLMConfig to only parse environment when it is available #91

0.1.17

Bug fix

  • Fixes loading logics from custom path. If given model path are given, OpenLLM won't try to import it to the local store.

    OpenLLM now only imports and fixes the models to loaded correctly within the bento, see the generated service for more information.

    Fixes service not ready when serving within a container or on BentoCloud. This has to do with how we load the model before in the bento.

    Falcon loading logics has been reimplemented to fix this major bug. Make sure to delete all previous save weight for falcon with openllm prune

    openllm start now supports bento

    openllm start llm-bento --help

    #80

0.1.16

No significant changes.

0.1.15

Features

  • openllm.Runner now supports AMD GPU, addresses #65.

    It also respect CUDA_VISIBLE_DEVICES set correctly, allowing disabling GPU and run on CPU only. #72

0.1.14

Features

  • Added support for standalone binary distribution. Currently works on Linux and Windows:

    The following are supported:

    • aarch64-unknown-linux-gnu
    • x86_64-unknown-linux-gnu
    • x86_64-unknown-linux-musl
    • i686-unknown-linux-gnu
    • powerpc64le-unknown-linux-gnu
    • x86_64-pc-windows-msvc
    • i686-pc-windows-msvc

    Reverted matrices expansion for CI to all Python version. Now leveraging Hatch env matrices #66

Bug fix

  • Moved implementation of dolly-v2 and falcon serialization to save PreTrainedModel instead of pipeline.

    Save dolly-v2 now save the actual model instead of the pipeline abstraction. If you have a Dolly-V2 model available locally, kindly ask you to do openllm prune to have the new implementation available.

    Dolly-v2 and falcon nows implements some memory optimization to help with loading with lower resources system

    Configuration removed field: 'use_pipeline' #60

  • Remove duplicated class instance of generation_config as it should be set via instance attributes.

    fixes tests flakiness and one broken cases for parsing env #64

0.1.13

No significant changes.

0.1.12

Features

  • Serving LLM with fine-tuned LoRA, QLoRA adapters layers

    Then the given fine tuning weights can be served with the model via openllm start:

    openllm start opt --model-id facebook/opt-6.7b --adapter-id /path/to/adapters

    If you just wish to try some pretrained adapter checkpoint, you can use --adapter-id:

    openllm start opt --model-id facebook/opt-6.7b --adapter-id aarnphm/opt-6.7b-lora

    To use multiple adapters, use the following format:

    openllm start opt --model-id facebook/opt-6.7b --adapter-id aarnphm/opt-6.7b-lora --adapter-id aarnphm/opt-6.7b-lora:french_lora

    By default, the first adapter-id will be the default lora layer, but optionally users can change what lora layer to use for inference via /v1/adapters:

    curl -X POST http://localhost:3000/v1/adapters --json '{"adapter_name": "vn_lora"}'

    Note that for multiple adapter-name and adapter-id, it is recomended to update to use the default adapter before sending the inference, to avoid any performance degradation

    To include this into the Bento, one can also provide a --adapter-id into openllm build:

    openllm build opt --model-id facebook/opt-6.7b --adapter-id ...

    Separate out configuration builder, to make it more flexible for future configuration generation. #52

Bug fix

  • Fixes how llm.ensure_model_id_exists parse openllm download correctly

    Renamed openllm.utils.ModelEnv to openllm.utils.EnvVarMixin #58

0.1.11

No significant changes.

0.1.10

No significant changes.

0.1.9

Changes

  • Fixes setting logs for agents to info instead of logger object. #37

0.1.8

No significant changes.

0.1.7

Features

  • OpenLLM now seamlessly integrates with HuggingFace Agents. Replace the HfAgent endpoint with a running remote server.

    import transformers

agent = transformers.HfAgent("http://localhost:3000/hf/agent")  # URL that runs the OpenLLM server

agent.run("Is the following `text` positive or negative?", text="I don't like how this models is generate inputs")

    Note that only starcoder is currently supported for agent feature.

    To use it from the openllm.client, do:

    import openllm

client = openllm.client.HTTPClient("http://123.23.21.1:3000")

client.ask_agent(
    task="Is the following `text` positive or negative?",
    text="What are you thinking about?",
    agent_type="hf",
)

    Fixes a Asyncio exception by increasing the timeout #29

0.1.6

Changes

  • --quantize now takes int8, int4 instead of 8bit, 4bit to be consistent with bitsandbytes concept.

    openllm CLI now cached all available model command, allow faster startup time.

    Fixes openllm start model-id --debug to filtered out debug message log from bentoml.Server.

    --model-id from openllm start now support choice for easier selection.

    Updated ModelConfig implementation with getitem and auto generation value.

    Cleanup CLI and improve loading time, openllm start should be 'blazingly fast'. #28

Features

  • Added support for quantization during serving time.

    openllm start now support --quantize int8 and --quantize int4 GPTQ quantization support is on the roadmap and currently being worked on.

    openllm start now also support --bettertransformer to use BetterTransformer for serving.

    Refactored openllm.LLMConfig to be able to use with __getitem__: openllm.DollyV2Config()['requirements'].

    The access order being: __openllm_*__ > self.<key> > __openllm_generation_class__ > __openllm_extras__.

    Added towncrier workflow to easily generate changelog entries

    Added use_pipeline, bettertransformer flag into ModelSettings

    LLMConfig now supported __dataclass_transform__ protocol to help with type-checking

    openllm download-models now becomes openllm download #27