Nathan Lambert

natolambert

AI & ML interests

Reinforcement learning, Ethics, Robotics, Dynamics Models

Recent Activity

updated a collection about 9 hours ago
Artifacts 7
liked a dataset about 9 hours ago
simplescaling/s1K-1.1
updated a collection about 23 hours ago
Artifacts 7
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Ai2's profile picture Society & Ethics's profile picture MBRL Library's profile picture Simulation Environments Tests and Builds's profile picture BigCode's profile picture πŸ€— H4 Community's profile picture CompVis Community's profile picture Explorer of Simulate alpha's profile picture University of Washington NLP's profile picture calmcode's profile picture Berkeley-Nest's profile picture OLMo Friends's profile picture Brrr Gang's profile picture AI2 Adapt Dev's profile picture Social Post Explorers's profile picture OLMoE's profile picture Interconnects AI's profile picture

natolambert's activity

reacted to banghua's post with πŸ€— 11 months ago
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1548
Have we really squeezed out the capacity of a compact chat model? Thrilled to see our latest open model, Starling-7B, ranks 13th among all models in Chatbot Arena!
πŸš€ As a 7B model, Starling surpasses larger open and proprietary models, including Claude-2, GPT-3.5-Turbo, Gemini Pro, Mixtral 8x7B and Llama2-70B, and is currently the best 7B chat model in Chatbot Arena!
Try out the model on HF here: Nexusflow/Starling-LM-7B-beta
replied to julien-c's post 11 months ago
reacted to julien-c's post with 🀯 11 months ago
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What if you could casually access your remote GPU in HF Spaces from the comfort of your local VSCode 🀯
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reacted to vikhyatk's post with ❀️ 11 months ago
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Just released moondream2 - a small 1.8B parameter vision language model. Now fully open source (Apache 2.0) so you can use it without restrictions on commercial use!

vikhyatk/moondream2
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reacted to yuchenlin's post with ❀️ about 1 year ago
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Introducing Vision Arena (beta)! Based on the lmsys's ChatbotArena, we create a simple demo for testing different Vision LMs (VLMs). We now support GPT-4V, Gemini-Pro-Vision, and Llava. More updates and models will come soon! We are still in the development stage and for now and we'd love to hear your feedback and suggestions! Please help us vote for better VLMs in your own use cases here! :D Kudos to Yujie Lu (UCSB)!
WildVision/vision-arena
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reacted to andrewrreed's post with πŸ€— about 1 year ago
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πŸš€ It's now easier than ever to switch from OpenAI to open LLMs

Hugging Face's TGI now supports an OpenAI compatible Chat Completion API

This means you can transition code that uses OpenAI client libraries (or frameworks like LangChain 🦜 and LlamaIndex πŸ¦™) to run open models by changing just two lines of code πŸ€—

⭐ Here's how:
from openai import OpenAI

# initialize the client but point it to TGI
client = OpenAI(
    base_url="<ENDPOINT_URL>" + "/v1/",  # replace with your endpoint url
    api_key="<HF_API_TOKEN>",  # replace with your token
)
chat_completion = client.chat.completions.create(
    model="tgi",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Why is open-source software important?"},
    ],
    stream=True,
    max_tokens=500
)

# iterate and print stream
for message in chat_completion:
    print(message.choices[0].delta.content, end="")


πŸ”— Blog post ➑ https://huggingface.co/blog/tgi-messages-api
πŸ”— TGI docs ➑ https://huggingface.co/docs/text-generation-inference/en/messages_api
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posted an update about 1 year ago
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Today, we’re releasing our first pretrained Open Language Models (OLMo) at the Allen Institute for AI (AI2), a set of 7 billion parameter models and one 1 billion parameter variant. This line of work was probably the main reason I joined AI2 and is the biggest lever I see possible to enact meaningful change in how AI is used, studied, and discussed in the short term.

Links at the top because that's what you want:
* Core 7B model: allenai/OLMo-7B
* 7B model twin (different GPU hardware): allenai/OLMo-7B-Twin-2T
* 1B model: allenai/OLMo-1B
* Dataset: allenai/dolma
* Paper (arxiv soon): https://allenai.org/olmo/olmo-paper.pdf
* My personal blog post: https://www.interconnects.ai/p/olmo


OLMo will represent a new type of LLM enabling new approaches to ML research and deployment, because on a key axis of openness, OLMo represents something entirely different. OLMo is built for scientists to be able to develop research directions at every point in the development process and execute on them, which was previously not available due to incomplete information and tools.

Depending on the evaluation methods, OLMo 1 is either the best 7 billion parameter base model available for download or one of the best. This relies on a new way of thinking where models are judged on parameter plus token budget, similar to how scaling laws are measured for LLMs.

We're just getting started, so please help us learn how to be more scientific with LLMs!
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