cat /dev/llama is not yet implemented. However we have a module to which you can give a prompt as parameter. It's buggy now.
Our goal is to write a proper kernel module to implement three things:
1. a character device
2. 1st backend is a LLMZip ie you write to say /dev/l2ezip, you get a compressed stream out
3. 2nd backend is a LLM, ie you write a prompt to say /dev/llama2, you get a completion back
So the 1st backend could be useful for compressed telemetry
The second backend could be useful for IoT LLM, or our ambitious plan of responding to telemetry, ie take action, such as control motor speed etc.
> My research goal is to train models using various hardware telemetry data with the hope that the models learn to interpret sensor inputs and control actuators based on the insights they glean from the sensor inputs. This research direction may open up exciting possibilities in fields such as automation, space, robotics and IoT, where L2E can play a pivotal role in bridging the gap between AI and physical systems.
This part was easy to miss but quite interesting, could you expand a bit here? What does L2E stand for?
thanks! I am still curious about the sensor inputs part.
I am trying to replace the 2.4ghz controller on my electric skateboard to make 0 to 5kmh and braking more pleasant and maybe use gyroscopes to do away with the controller altogether. What would tokens be in that case? Do you create a CAN style representation and feed that to the llm? What kind of throughput do you foresee being possible on which hardware?
In this case, I would ask the LLM to suggest an algorithm to minimize acceleration, jerk and snap based on the expected sensor input data, and then just implement that. Probably in memory on whatever runs the board.
Straightforward control problem of bringing the board from 5-0kmh smoothly?
Basic control theory will work better than AI here. The mathematical models used in control theory have been used in computing since at least the 50's (Kalman Filters). I suspect you won't have issues with computational power.
Figuring out exactly which model to use and how, may take some work. Also understanding control theory will allow you to do things like traction control, etc.
This could be a lot clearer about which install methods are offered. It seems to be an OS that you have to boot into a VM? What are the system requirements for the container?
It is a great start for alpha. While I agree with poster above you, I absolutely do not want you to get discouraged. This is awesome and can only get better from here.
No but each version of L2E OS has a name. v0.1 is the first version and we wanted to pay our tribute to Terry A Davis, so called it TempleDOS and added a bit of TempleOS references.
> How do we make sure that the output is factual and not hallucinated?
One method the readme doesn't mention: ask the same question multiple times. Apparently research suggests that when LLMs hallucinate answers, their hallucination is likely to be a different one every time, where as factual answers will tend to be consistent.
Purely anecdotally this is not a very successful method in my usage
To reproduce: I've had it be consistent on hallucinations on the stable homotopy groups of spheres; on the quote "who speaks of victory to endure is all"; and many other fact based questions. Model: gpt4
Asking repeatedly would be impractical. I have some vague idea in my mind called the fact engine. It's like a wiki where people could key in facts. Also facts based on "timeframes", ie 1800's 1900's etc (eg Moon Landing). The fact engine feeds a model that checks for hallucinations, and also rule based engines. Then a final score would be assigned based on consensus. Something like that.
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[ 0.24 ms ] story [ 112 ms ] threadOur goal is to write a proper kernel module to implement three things:
1. a character device 2. 1st backend is a LLMZip ie you write to say /dev/l2ezip, you get a compressed stream out 3. 2nd backend is a LLM, ie you write a prompt to say /dev/llama2, you get a completion back
So the 1st backend could be useful for compressed telemetry The second backend could be useful for IoT LLM, or our ambitious plan of responding to telemetry, ie take action, such as control motor speed etc.
> My research goal is to train models using various hardware telemetry data with the hope that the models learn to interpret sensor inputs and control actuators based on the insights they glean from the sensor inputs. This research direction may open up exciting possibilities in fields such as automation, space, robotics and IoT, where L2E can play a pivotal role in bridging the gap between AI and physical systems.
This part was easy to miss but quite interesting, could you expand a bit here? What does L2E stand for?
I am trying to replace the 2.4ghz controller on my electric skateboard to make 0 to 5kmh and braking more pleasant and maybe use gyroscopes to do away with the controller altogether. What would tokens be in that case? Do you create a CAN style representation and feed that to the llm? What kind of throughput do you foresee being possible on which hardware?
Telemetry in sense sensor streams, both command and responses.
Then we'll just train a small model for long enough. Then we will see how it would respond.
That's the plan sort of.
Straightforward control problem of bringing the board from 5-0kmh smoothly?
Figuring out exactly which model to use and how, may take some work. Also understanding control theory will allow you to do things like traction control, etc.
um...
We need to go deeper!
Qemu:
qemu-system-x86_64 -display gtk,zoom-to-fit=off -m 512 -accel kvm -vga virtio -cdrom l2eos.iso
I gave up - so confusing.
If you want to build it yourself, you can do make l2e_os_iso if you clone the repo and you are on a linux machine.
This v0.1 and consider it alpha. A lot of stuff is broken.
The current build just needs 512MB RAM and an x86_64 CPU.
The ISO can be downloaded from the releases here:https://github.com/trholding/llama2.c/releases/tag/L2E_OS_v0...
Currently it doesn't do much useful stuff except for stories.
You could etch the ISO to pendrive and boot on real system or you could run it in qemu/VM too:
qemu-system-x86_64 -display gtk,zoom-to-fit=off -m 512 -accel kvm -vga virtio -cdrom l2eos.iso
There is a known issue that for some it does not work on Virtual Box. All that will be fixed in future versions.
There are also easter eggs and a hidden game of DOOM (Freedom)
Pics: https://twitter.com/VulcanIgnis/status/1708851772435968017
https://www.reddit.com/r/LocalLLaMA/comments/16zklam/i_creat...
User posted Video of Doom Play: https://www.reddit.com/user/multiverse_fan/comments/170orkz/...
I mean, obviously there are benefits from having the code, but most of the time people will give you an executable.
Why isnt this the same?
Instructions also are there too
One method the readme doesn't mention: ask the same question multiple times. Apparently research suggests that when LLMs hallucinate answers, their hallucination is likely to be a different one every time, where as factual answers will tend to be consistent.
https://arxiv.org/abs/2305.18248
To reproduce: I've had it be consistent on hallucinations on the stable homotopy groups of spheres; on the quote "who speaks of victory to endure is all"; and many other fact based questions. Model: gpt4
Couple related things in this direction that may be leveragable:
- WikiData: https://m.wikidata.org/wiki/
- Knowledge Futures' Underlay: https://docs.underlay.org/history