LLMs

How do models represent style, and how can we more precisely extract and steer it?

A commonly requested feature in almost any LLM-based writing application is “I want the AI to respond in my style of writing,” or “I want the AI to adhere to this style guide.” Aside from costly and complicated multi-stage finetuning processes like Anthropic’s RL with... See more

How can we make interacting with conversational models feel more natural?

Every conversational interface to a language model adopts the same pattern:

A chat history sidebar, with each conversation lasting just a few turns

New sessions always begin in a brand-new thread

Every user query must always elicit exactly one response

None of these assumptions... See more

The context size of the input is too small for when you want to analyse CSV's with 1000's of rows and embedding doesn't really work because it loses context.

However development time, and maintenance can offset these savings. Hiring skilled data scientists, machine learning engineers, and DevOps professionals can be expensive and time consuming. Using available resources for “reimplementing” solutions hinder innovation and lead to a lack of focus. Since You not longer work on improving your model or... See more

Study finds RLHF reduces LLM creativity and output variety : A new research paper posted in /r/LocalLLaMA shows that while alignment techniques like RLHF reduce toxic and biased content, they also limit the creativity of large language models, even in contexts unrelated to safety.

MLServer aims to provide an easy way to start serving your machine learning models through a REST and gRPC interface, fully compliant with KFServing's V2 Dataplane spec. Watch a quick video introducing the project here.

• Multi-model serving, letting users run multiple models within the same process.


• Ability to run inference in parallel for vertical

... See more

OpenAI is treating its new marketplace seriously now: The brand new GPT store will come with REVENUE SHARING.... (missing in the Plugins launch)

and launching a Stateful Assistants API:

- Persistent Threads (/api/openai/threads)

- Built in Retrieval (chunking etc done for you)

- Code Interpreter (RIP Adv Data Analysis?)

- Speech to Text and Text to... See more

A new v0.4.0 release of lm-evaluation-harness is available !

New updates and features include:

• Internal refactoring


• Config-based task creation and configuration


• Easier import and sharing of externally-defined task config YAMLs


• Support for Jinja2 prompt design, easy modification of prompts + prompt imports from Promptsource


• More advanced configuration

... See more

Deploying a Generative AI model requires more than a VM with a GPU. It normally includes:

• Container Service : Most often Kubernetes to run LLM Serving solutions like Hugging Face Text Generation Inference or vLLM.


• Compute Resources : GPUs for running models, CPUs for management services


• Networking and DNS : Routing traffic to the appropriate

... See more