First Impressions of Gemini Flash 1.5 - The Fastest 1 Million Token Model
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- Опубліковано 8 чер 2024
- Just checked out Google's new Gemini Flash at Google I/O. It's a super-fast AI model designed for handling big tasks - think processing videos, audios, or huge codebases, all while keeping costs low. I put it through its paces against giants like GPT 3.5 and GPT 4.0, looking at performance, costs, and how it handles real-world tasks. I even tried confusing it with tricky questions and coding challenges in Google AI Studio. Spoiler: it's not perfect, but for speed and efficiency on a budget? Pretty cool stuff. Stick around to see how Gemini Flash holds up in the AI arena!
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TIMESTAMPS:
00:00 Introducing Gemini Flash: Google's Answer to GPT-4
00:53 Why Choose Gemini Flash? Performance and Cost Analysis
02:20 Hands-On Testing: Unveiling Gemini Flash's Capabilities
03:05 Exploring Safety Features and Customization
03:44 Prompt-Based Testing: Analyzing Model Responses
12:11 Advanced Testing: Contextual Understanding and Mathematics
15:30 Programming Challenges: Assessing Gemini Flash's Coding Prowess
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What software did you use to create this video recording, looks interesting
Awesome analysis, thank you!
thank you for your video! Quick question:
I'm trying to use the Gemini 1.5 Pro API and to host it on a Streamlit app. is it possible if we could host the Gemini 1.5 Pro API on streamlit to receive video file as inputs from users and perform the desired output (to get summary or whatever). Basically just like what we have in google studio but using the API in the streamlit app. Is it possible?
I think its not possible yet but you can have a work around. You can generate frames and then use those in sequence to get response. Here is one way of doing it: tinyurl.com/5n8ywwpt
Could the Gemini Flash 1.5 model handle most of the heavy lifting, allowing 4.0 to manage more complex, value-oriented tasks, such as image recognition? For instance, could Gemini handle routine tasks and delegate more complex ones to 4.0 when necessary?In an agent-based problem-solving chatbot with a RAG implementation, wouldn't this approach be more cost-effective?I'm working on a grow buddy chatbot for plant health and environmental monitoring, which includes image recognition. The backend is being developed in Python, running on a cloud server, with communication to an Android front-end via RESTful APIs.I'm relatively new to coding, having started just six months ago. Any advice or insights tha5 anyone can provide would be greatly appreciated!
P.s. It's the implementation of vision recognition is where I'm completely stuck at. 😔
Is it possible to use GEMINI API in visual studio code?
Yes, the code segment you get from ai studio shows how to set that up.
nice model.
.Interesting, it seems both OpenAI & Google are stepping up their game, improving performance, speed, and benchmarks a bit. But no groundbreaking intelligence yet. Is this a plateau for transformer models? Or are these companies slowing down for steady competition? Either way, it's cool that the tech behind these models isn't a secret anymore. KV Cache, RoPE and ... Watching your video got me thinking, how cool would it be to invest in something like Llama3-Flash with a 200k content window length! 🚀
That's the beauty of open source :) I still think the main limiting factor would be the availability of compute! but the way I think about it is the for most use cases, you don't even need SOTA models. A specialized Llama3-Flash-200k will be more than enough :)
That is exactly what I believe. Giant companies aim to dominate the AGI market for future valuation, but we need to focus on “topic-specific language models” and orchestrate dozens of them, from 100M to 1B. This is the way to build a distributed “intelligent” system, similar to blockchain where each block is a small model. I think this is the way to AGI, making it more consumer-device friendly and energy-efficient. As Mandalorians say, this is the way.@@engineerprompt
geimini 1.5 pro vs gemini 1.5 pro flash what is better ?
the pro version is better but slower.
it is amaze me when transformers does that... I remember when stability AI never be able to logic the human fingers even it's already "seen" them... I wonders does it think human hands have so many fingers because everytime prompt come in it's instant whole tokens. and since "it" limited by cpu bottle neck which makes "it" think all being have 1 hands to hold a thing and only able to out put something one at a time
(please forget this talk I'm hallucinating... too much time with them made me thinks to hallucinate too, it... it... some what fun to hallucinate,.... words )
yes, in my project i use is so fast, really like this future, like your video
looking for a better chatGPT 3.5 in the same price range, I m impressed with Gemini Flash..
options are always better :)
You forgot to hide your email
Mistral Large is died 😅
They might release a multimodal.
Gemini is shit.. GPT4o the best !
In production, wouldn't using 4.0 be super expensive, especially for a personal assistant with vision recognition capabilities?Could the Gemini Flash 1.5 model handle most of the heavy lifting, allowing 4.0 to manage more complex, value-oriented tasks, such as image recognition? For instance, could Gemini handle routine tasks and delegate more complex ones to 4.0 when necessary?In an agent-based problem-solving chatbot with a RAG implementation, wouldn't this approach be more cost-effective?I'm working on a grow buddy chatbot for plant health and environmental monitoring, which includes image recognition. The backend is being developed in Python, running on a cloud server, with communication to an Android front-end via RESTful APIs.I'm relatively new to coding, having started just six months ago. Any advice or insights you can provide would be greatly appreciated!Thank you for your time!
@caseyhoward8261 Yes, it would be more expensive, and absolutely, it would be. Ironically, Gemini has a larger token capacity than the GPT4o having an overall capacity of 128,000, Gemini, exceeds this with a token capacity of 1,000,000 making it far more superior.
@@jakeboardman5212Thank you.
Not the same price. Flash seem better than Haiku and better than mistral