6 items tagged “cerebras”
2024
Cerebras Coder (via) Val Town founder Steve Krouse has been building demos on top of the Cerebras API that runs Llama3.1-70b at 2,000 tokens/second.
Having a capable LLM with that kind of performance turns out to be really interesting. Cerebras Coder is a demo that implements Claude Artifact-style on-demand JavaScript apps, and having it run at that speed means changes you request are visible within less than a second:
Steve's implementation (created with the help of Townie, the Val Town code assistant) demonstrates the simplest possible version of an iframe sandbox:
<iframe
srcDoc={code}
sandbox="allow-scripts allow-modals allow-forms allow-popups allow-same-origin allow-top-navigation allow-downloads allow-presentation allow-pointer-lock"
/>
Where code is populated by a setCode(...) call inside a React component.
The most interesting applications of LLMs continue to be where they operate in a tight loop with a human - this can make those review loops potentially much faster and more productive.
Pelicans on a bicycle. I decided to roll out my own LLM benchmark: how well can different models render an SVG of a pelican riding a bicycle?
I chose that because a) I like pelicans and b) I'm pretty sure there aren't any pelican on a bicycle SVG files floating around (yet) that might have already been sucked into the training data.
My prompt:
Generate an SVG of a pelican riding a bicycle
I've run it through 16 models so far - from OpenAI, Anthropic, Google Gemini and Meta (Llama running on Cerebras), all using my LLM CLI utility. Here's my (Claude assisted) Bash script: generate-svgs.sh
Here's Claude 3.5 Sonnet (2024-06-20) and Claude 3.5 Sonnet (2024-10-22):
Gemini 1.5 Flash 001 and Gemini 1.5 Flash 002:
GPT-4o mini and GPT-4o:
o1-mini and o1-preview:
Cerebras Llama 3.1 70B and Llama 3.1 8B:
And a special mention for Gemini 1.5 Flash 8B:
The rest of them are linked from the README.
llm-cerebras. Cerebras (previously) provides Llama LLMs hosted on custom hardware at ferociously high speeds.
GitHub user irthomasthomas built an LLM plugin that works against their API - which is currently free, albeit with a rate limit of 30 requests per minute for their two models.
llm install llm-cerebras
llm keys set cerebras
# paste key here
llm -m cerebras-llama3.1-70b 'an epic tail of a walrus pirate'
Here's a video showing the speed of that prompt:
The other model is cerebras-llama3.1-8b.
Cerebras Inference: AI at Instant Speed (via) New hosted API for Llama running at absurdly high speeds: "1,800 tokens per second for Llama3.1 8B and 450 tokens per second for Llama3.1 70B".
How are they running so fast? Custom hardware. Their WSE-3 is 57x physically larger than an NVIDIA H100, and has 4 trillion transistors, 900,000 cores and 44GB of memory all on one enormous chip.
Their live chat demo just returned me a response at 1,833 tokens/second. Their API currently has a waitlist.
2023
Downloading and converting the original models (Cerebras-GPT) (via) Georgi Gerganov added support for the Apache 2 licensed Cerebras-GPT language model to his ggml C++ inference library, as used by llama.cpp.
Cerebras-GPT: A Family of Open, Compute-efficient, Large Language Models (via) The latest example of an open source large language model you can run your own hardware. This one is particularly interesting because the entire thing is under the Apache 2 license. Cerebras are an AI hardware company offering a product with 850,000 cores—this release was trained on their hardware, presumably to demonstrate its capabilities. The model comes in seven sizes from 111 million to 13 billion parameters, and the smaller sizes can be tried directly on Hugging Face.