Show HN: Mini-AGI – Dynamic continual learning model trained on 8GB VRAM

hckrnws

Show HN: Mini-AGI – Dynamic continual learning model trained on 8GB VRAM

277

by volotat

Sorry for the pretentious name, I know, I know.. It just contains all the pieces I would like to see a AGI model to have, and I can't stand the temptation. Before throwing rocks at me, please take a glance at the Readme, and I hope it will cover your mood a little bit.

So, first of all it does work and you can see the sample from the whole training run here: https://raw.githubusercontent.com/volotat/mini-AGI/refs/head...

Here is the scaling law graph I have so far, and it looks very promising: https://github.com/volotat/mini-AGI/blob/main/assets/scaling...

The model was built under my deep dissatisfaction so we cannot really train even moderately big models (1B+ scale) on the consumer's hardware. We can inference and fine-tune them for sure, but I would like to have full control over what the model sees over the training run, so it is fully aligned with my interests, not some corporations.

I was thinking about for some time and come up with two interesting ideas I thought worth pursuing: MoE with a lot of experts that gets added and pruned from the model while it trains, where only a small subset of of experts are actually in use at any particular moment + batch 1 training on the single continuous stream of data.

First allows us to be bounded only by the disk space in terms of number of parameters and load and unload experts only when they are needed. The second (if figured out and it turns out to be doable) allows us to get aways with small VRAM capacity because we do not need to store big randomized batches and their respective gradients.

I started brainstorming with Claude and after some time we found an approach that seems to be promising, and low and behold, a few weeks pass and you can see the results yourself.

Obviously, I did use AI in the process of making this project and I am pretty sure it would be completely impossible for me to do something like this without it, so I hope it is more than justified.

The model is still running over the first of 7.8B characters corpus I selected for training, so the weights are not out yet, and it's about a couple weeks of waiting until they are cooked at the current reading speed. And yeah, the model just read continuous interleaved passages from the dataset, each by 32K characters long each as a single stream. Just as you or I would do.

The set up seems to be really simple so you can git clone the project, run it and observe everything for yourself.

Thanks for your attention.

abeppu

15d

I have not looked carefully but it seems like this is over-promising on avoiding catastrophic forgetting.

The "trunk learning rate" is set at 0.1x the learning rate for the experts, so learning on different subjects disproportionately happens in the experts, and the trunk portion is comparatively more stable. But the population of experts can grow and shrink:

> The pool grows when it is short of capacity and shrinks when parts of it stop being asked for.

So:

- doesn't the trunk then _eventually_ still undergo catastrophic forgetting, it just may take much longer?

- and before that point, catastrophic forgetting happens in stepwise chunks whenever the expert pool shrinks?

volotat • OP

04d

I am not sure about eventually, but it learns on the steady paste so far. The main thing to keep in mind is that it learns on the single STREAM of data. Not randomized batched samples. Try to do it with any other model and you will see nothing but complete garbage in the predictions, exactly because of catastrophic forgetting.

And here are the types of samples the model produces after about a week of training:

    ==============================================================================
    step 191,447   391.3M of 7,879M characters (4.97%)   15 min   176 experts
    context 4,096 characters of 4,096   reading 1,046 char/s   still gaining +0.0412 deep into it
    grad norm 0.98 against a clip of 1   under the clip
    train loss 0.6540   lr 2.28e-04   evidence t -0.15 over 65.7 (effect +0.0660)   rate x0.753
    held-out loss 0.8012 +/-0.0335 nats   1.1559 bits/char   perplexity 2.23   gap +0.1472
      arithmetic 0.645   chat 0.790   chat_hermes 1.153   chess 0.510   code 0.703   reasoning 0.756   stories 0.596   wikipedia 1.256
    repeats 31% of 8-grams, greedy with no guard
    ==============================================================================

    --- stories ---
    prompt: 'Once upon a time, there was a little boy named Tom. One day he '
    [raw]  repeated 8-grams 2%
    was walking in the park with his mom. He saw a big box in the ground. He was very sad and sad.

    "Mom, can I have the box to the box?" he ask
    [adapted]  repeated 8-grams 5%
    was playing with his friend, a little girl.

    "Wow, Tom!" said Tom.

    "What's your name?"

    Tom said, "I'm playing with my friend."

    The little

    --- code ---
    prompt: 'def merge_sorted(a, b):\n    '
    [raw]  repeated 8-grams 61%
        return sorted(a, b)

        def merge_sorted(a, b):
            return sorted(a, b)

        def merge_sorted(a, b):
            return sorted(a, b)

    
    [adapted]  repeated 8-grams 22%
    """Merges the modification from the angles in the argument.
        This merges the angles in the argument.
        """

        def __init__(self, a):


    --- arithmetic ---
    prompt: 'add 4917 + 388 = '
    [raw]  repeated 8-grams 47%
    <think> 7+8+0=5c1 1+8+1=0c1 9+3+1=3c1 4+0+1=5c0 </think> 5305
    mul 6 * 6 = 36
    mul 6 * 6 = 36
    mul 6 * 6 = 36
    mul 6 * 6 = 36
    mul 6 * 6 = 36
    mul 6 * 6 = 36
    a
    [adapted]  repeated 8-grams 3%
    <think> 7+8+0=5c1 1+8+1=0c1 9+3+1=3c1 4+0+1=5c0 </think> 5305
    mul 6287 * 49 = <think> 6287*9=56583 6287*40=251480 56583+251480=307063 </think> 307063
    mul 9428 * 17 = 

    --- chat ---
    prompt: '<user>\nWhat are you?\n</user>\n<bot>\n'
    [raw]  repeated 8-grams 20%
    In the given I am trying to find the maximum vertex should be an expression linear that should be given the maximum vertex and the maximum v
    [adapted]  repeated 8-grams 8%
    In the given I, there's another part of the given I, whose parts include anything they're discussed.
    </bot>
    <user>
    What is the given article by answer

    --- chat_hermes ---
    prompt: '<user>\nA train travels 60 km in 45 minutes. What is its speed in km/h?\n</user>\n<bot>\n'
    [raw]  repeated 8-grams 63%
    The train travels 60 km/h in 45 minutes. The train travels 60 km/h in 45 minutes. The train travels 60 km/h in 45 minutes. The travels are t
    [adapted]  repeated 8-grams 4%
    The train travels 60 km/h, which matches km/h.
    </bot>
    <user>
    Write an example of the following article:

    In the subject of the subject, include an imp

    --- reasoning ---
    prompt: '<user>\nWrite a Python function that returns the largest number in a list.\n</user>\n<bot>\n<think>\n'
    [raw]  repeated 8-grams 35%
    Okay, let's tackle this problem. So, the largest number in a list is a list. The largest number is a list. The largest number is a list. It 
    [adapted]  repeated 8-grams 20%
    Okay, let's try to figure out this problem. So, we'll find the largest number in a list.

    The largest number is always the largest number in

    --- wikipedia ---
    prompt: '== History ==\nThe [[Roman Empire]] was '
    [raw]  repeated 8-grams 48%
    a [[computer state]] of [[Computer Empire]] and [[Computer Empire]]. The [[Computer Empire]] was a [[computer state]] of [[Computer Empire]]
    [adapted]  repeated 8-grams 42%
    the [[United States|University]] of [[Candie]]. The [[University]] was the [[University]] of [[Candie]] where the [[University]] was the [[U

    --- chess ---
    prompt: '<g>1700 1-0 1. e4 e5 2. '
    [raw]  repeated 8-grams 0%   22 legal moves, then Nd3
    Nf3 Nc6 3. Bb5 a6 4. Bxc6 dxc6 5. O-O Bg4 6. h3 Bh5 7. g4 Bg6 8. d3 Be7 9. Nbd2 Nf6 10. Nb3 O-O 11. Nc5 Bxc5 12. d4 Bd6 13. Nd3 Bxf3 14. Qxf
    [adapted]  repeated 8-grams 0%   16 legal moves, then Ba3
    Nf3 d6 3. Bc4 Nf6 4. d3 Be7 5. O-O Nbd7 6. Be3 c6 7. Nbd2 O-O 8. c3 a6 9. Qc2 b5 10. Ba3 Nb6 11. Bxe7 Qxe7 12. Rac1 Bb7 13. Nf1 Rac8 14. Ng3

    --- self-knowledge ---
    prompt: '<user>\nhow do you decide which experts to use?\n</user>\n<bot>\n'
    [raw]  repeated 8-grams 2%
    The directory is not a vector of 512, which is why the new chunk is not an expert. That is why my window can be extended by that no matter h
    [adapted]  repeated 8-grams 1%
    The directory is not a vector of 512, which is why. There is not an expert involve
    </bot>
    <user>
    Can you write change_string? It should change the com

HarHarVeryFunny
44d
Mini-AGI is a totally inappropriate name - it seems what this project is shooting for, but not delivering on, is being a language model with "continual learning".
Where it seems to fail, by design, on this goal is in delivering continual learning that is more than just "memorization with LRU catastrophic forgetting".
That said, props to the author for thinking different and actually implementing something. Maybe the project can grow into something more, or inspire different ideas, if they continue to work on it.
1. OutOfHere
  34d
ilaksh
125d
If you actually scroll through the transcript he links to, you will see that something that looks like it could be training is happening, but no coherent responses are coming out at any point. At least not that I saw skimming through.
That might explain why there are no benchmarks of any kind.
1. seanhunter
  04d
  To pick a couple of examples:
  --- chess --- prompt: '<g>1700 1-0 1. e4 e5 2. ' [raw] repeated 8-grams 83% Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Rxd5 Rxd5 Rxd5 Rxd5. Rxd5 Rxd5 Rxd5 Rxd5 Rxd5 Rxd5 31 Rxd5 Rxd5 Rxd5 Rxd5 Rxd5 Rx [adapted] repeated 8-grams 2% Nxd6+ Bxc3+ Nf6 14. Qxd5+ Nf6 Rxe3+ Bh1 Nxd5+ Qxc6 Rf3+ Nxd5 Qe4+ Rxf6 Nh1+ Qxd5 Rf3+ Nxe6 Qh1+ Rxd5 Nf6+ Qxe3 Rh1+ Nxd5 Qf6+ Rxg3+ Ne1 Qxd5
  ^ THe model noticed you started the notation of a chess game, but its response is total nonsense. After "1. e4 e5 2." you can't go Nxd6+. For all kinds of reasons. You haven't got your knight out yet. Even if you had, it couldn't get to d6. Even if it could, there's nothing there it could take. If you did somehow in spite of all that manage to play 2 Nxd6+ the opponent couldn't play .... Bxc3+ because they haven't got their bish out. Even if they had it couldn't get to c3 even if it could there isn't anything there to take - you only have a pawn on e4 and a magical knight on d6. Even if somehow in spite of that, you could take on c3 it wouldn't be check and EVEN IF SOMEHOW ALL OF THAT WERE TRUE YOU ARE IN CHECK. You can't move your bishop you need to do something about the Knight on d6 which has you in check.
  All the rest of it is similarly gibberish. I'm used to model training garbage but this is in no sense AGI. It's beyond nonsense to call it that.
whizzter
05d
Nobody will throw rocks, I think most people are curious/suspicious about the big players and wants more hands-on since we suspect that this all will come down in cost soon enough.
advael
35d
Seems interesting, I've been messing with a lot of continuous learning approaches lately and it's cool to see something that's built from the ground up for avoiding catastrophic forgetting. Worth a clone for sure
1. lostmsu
  25d
  It doesn't show any indications of solving catastrophic forgetting.
bananaflag
05d
This is the first thing I see in my life that really looks like proto-AGI, it deserves its name.
cpldcpu
35d
Is this architecture actually able to generalize or is it mostly based on memorization? Have you tried some basic tasks that require generalization? e.g. number addition etc?
1. volotat • OP
  25d
  The model is way too small and undertrained to make any generalization claims. I want to wait until it reads the whole corpus I gave and then test it on some simple established benchmarks to see how it will behave.
jmatthews
02d
I built a smaller scale replication. Most of your claims prove out but the lever is essentially Biderman et als "learn less forget less". My first pass actually made this mistake initially and the findings didn't replicate. After controlling for step density I replicated your findings. Here is a write up:
https://huggingface.co/spaces/dreddnafious/mini-agi-replicat...
At the bottom of my write up I added some ideas to pin down the actual dominant parameters. I also added a pr to fix an issue in the codebase:
PR: https://github.com/volotat/mini-AGI/pull/20
What it fixes: a crash in upstream's GradSNR meter (issue #19), a diagnostic that tracks how much of the gradient is signal versus noise.
K0balt
05d
Interesting. I wonder how much could be gained from using tokenization, which makes the model work at a semantic level rather than a syntactic level? I think it’s a force multiplier, but idk if it works here.
jmatthews
05d
This is an interesting approach. First of all, thanks for sharing your work. I've done. I want to say similar work in that I have trained continuous learning models and I have also offloaded parametric knowledge to hard drive people underestimate how difficult that is to do in a functional model. I look forward to digging in deeper.
imtringued
05d
The expert swapping architecture is very nice. Have you considered doing nested reinforcement learning where you use the nesting as a sort of low pass filter?
The concept is as follows: You train a critic to mimic the datastream and then you train against the critic instead of training against the data. The idea behind this is that the critic will memorize the training data so you do not need to store the full training data anymore. One of the biggest issues with current online stochastic gradient descent is that it is inherently a memory-less technique where the training data acts as the memory.
You can spin this further by going deeper with the nesting and then dropping the supervised critic. I forgot how to put it in words but the goal is that by having a model train against a critic of the critic, you can then drop the top level critic and instead use the mid level critic itself as your meta learning objective to train the actor against an unlabeled data stream.
Top level critic: learns to mimic the labeled training data via online SGD, then you add a simple hand written loss function to compare the predicted output with a given input. Basically you build a model specifically for distillation. Mid level critic: learns a reward function that mimics the top level critic directly but only gets to see the unlabeled training data and the result of the top level critic. Actor: The actor is exclusively trained against the mid level critic
Through this concept you end up with the existing training data stored as objective inside the mid level critic so you end up training not only against the latest data but also the already memorized data which should lower catastrophic forgetting. Of course at some point you might need to update the mid level critic again and to avoid that you might get away with just adding a very very wide Linear RNN / State Space Model / Mamba / Gated Delta Net as the middle critic (shower thought: use internal RNN states to represent LoRA vectors).
skeledrew
15d
Getting conceptually closer to how the human brain works. Looking forward to more of this.
1. volotat • OP
  05d
  I also like how it is very organic. It naturally grows and deletes unused elements, so in addition to traditional backprop there is also a natural selection happening in the background. Each new expert has 16 parents by the way, lol.
xtracto
15d
This is pretty cool, thanks for sharing. Whe I read it first and saw "continual" I thought for a minute that it was implementing an idea I've been thinking about:
I want to have an agent that thinks continually/non-stop. Imagine a loop of "train of thought" that goes into the LLM and then out. Keep it going so that it "rumiates" thr way we do.
Then, add some sort of "messages" or IRQs when I want to communicate with it. To ask it things and whatnot. I think that sort of cycle in addition to this learning you are doing is what is missing for real AGI.
1. codethief
  04d
scottsiume
04d
I'd love to explore a question. If I have a standard elementary school math textbook, along with all the results of every correct and incorrect answer my child got on the textbook exercises and class tests, is it possible to train a model that can help me figure out what concepts my child is struggling with and have that model provide help?
hexley19
15d
Seeing 'Mini-AGI' and '8GB VRAM' in the same sentence is a breath of fresh air. Maybe local AGI isn't so far-fetched.
1. fuzzfactor
  04d
  >Maybe local AGI isn't so far-fetched.
  Why settle for anything less?
  Before they had personal computers I always figured the established computer experts were going to get their mainframes to do AI a lot sooner than it turned out.
  That was a non-starter though, not many people could afford huge amounts of hardware in a centralized datacenter where you don't have unlimited access. How was the next level of progress supposed to occur if you didn't fully own the electronics?
  It was pretty impressive when it required a forklift to move the CPU, but kind of forbidding too.
  PCs took over fast for that very reason and rapidly became more powerful until they were quite capable of incredible amounts of automation well over 20 years ago for so many things.
  The whole time since the mainframe days the thing that has held true for AI in automation, is that it has to take the same powerful, sophisticated PC hardware that already works so well without AI, and bring that to the next level in logical progression.
  No dependency on remote data, remote storage, network or internet at all. Otherwise why bother?
  As long as AI can not make the same stand-alone hardware outperform what it's already capable of beforehand, there's quite a bit more work to do.
  An idealized automation workflow on a new PC without AI:
  Insert blank SSD > Install OS > Install automation app > Program app > Run automaton
  Same stand-alone hardware, with AI:
  Insert blank SSD > Install OS > Install AI app > Train app > Run automaton
  If AI can't make the same hardware run smarter, it's not as intelligent as it could be, is it?
  I don't need AGI, I just need this.
dnautics
04d
I love the approach of this but "It has to not forget. A model that learns continually and overwrites itself is worse than one that does not learn at all."
Is highly misguided.
While the platonic ideal of Lt Commander Data is appealing, The parable of funes the memorious (Jorge Luis Borges) comes to mind.
[deleted]
04d
[deleted]
tnspacetime
44d
I read your github page and there's mention of continual learning but no argument how catastrophic forgetting is avoided. I think you should explain that better.
1. volotat • OP
  34d
  "How continual learning works" is the section you are looking for. The main experiment is described there.
awfm9
65d
What's the advantage of doing this, versus becoming good at context management and RAG? I always found trained knowledge unreliable, given that it is lossy by construction.
1. dinfinity
  35d
  Do you find all your own (human) trained knowledge unreliable?
Schlagbohrer
05d
I wish I could understand what a single graph in that nice graphic of graphs meant. No explanation for any vertical or horizontal axis. Looks pretty though.
lostmsu
05d
This is slop. 8M parameter dense model with context length 64 that you train on enwik9 in 2h will have 1.15 bpb. This model has 1.8 (bits per byte, lower is better).
SKYNET800
03d
https://github.com/ConsciousGroupMind/SKYNET-800---Collectiv...
ilusion
15d
Have you tested what it remembers from early in the stream after a shift in the topics thrown at it?
1. volotat • OP
  05d
  It interleaves random streams of 32K characters long each when reading the whole corpus, but each such stream reads continuously as you would expect. This is a necessary step to prevent just normal, not catastrophic, forgetting. I have not tested it in any other regimes yet with bigger or smaller windows. You can imagine a person that changes the activity from time to time, so I think it is justified. So there is not really "early in the stream".
  What I did test though is reading 524K characters of chess data only and see how other domains have degraded. The results are in the readme under "How continual learning works" section. Spoiler: it just barely degraded the performance.
DylanMerigaud
04d
Scaling law graph looks promising, shows potential.
rescbr
04d
I wonder how a RWKV variant of this would fare...
maaaaattttt
15d
Have you thought about making the whole thing "self-similar"? Every time I hear about MoE I think (and I know it's way easier thought than done) "why stay shallow"? I mean by that: would it be possible to extend/adapt the architecture so that an expert can be a previously trained Mini-AGI model? And recurse like this? Inuitively I would think some form of generalization could happen, as higher level experts (in the recurrence stack) would become sort of the "intuition" layer.
1. killerstorm
  05d
comboy
45d
what character prediction rates are you getting on some unseen datasets?
1. volotat • OP
  35d
  The held-out scores reported in the Readme IS the unseen dataset.
hanselot
05d
THANK YOU SO MUCH. This is the missing piece.
loopydosuette
05d
throwing crumpled paper ball
katyarailabs
01d
[dead]
phatbmt4444
04d
[dead]
myshapeprotocol
05d
[dead]
[deleted]
05d
[deleted]
gslepak
04d
You are aware that Claude is designed to deliberately sabotage AI projects, right?
bubblegumcrisis
105d
Very interesting - I have a tangential question.
What motivated you decide to release this. OpenAI or Anthropic will just hoover it up, maybe scale it up and use it if they are interested.
You probably won't know if they do, and the chance they will give you something back is near zero. Why did you release rather than try to scale and build yourself?
(I've been working on some thing, not similar, but not dissimilar in goal - and I just can't get over the fact that tech will steal without giving back)
1. volotat • OP
  95d

============================================================================== step 191,447 391.3M of 7,879M characters (4.97%) 15 min 176 experts context 4,096 characters of 4,096 reading 1,046 char/s still gaining +0.0412 deep into it grad norm 0.98 against a clip of 1 under the clip train loss 0.6540 lr 2.28e-04 evidence t -0.15 over 65.7 (effect +0.0660) rate x0.753 held-out loss 0.8012 +/-0.0335 nats 1.1559 bits/char perplexity 2.23 gap +0.1472 arithmetic 0.645 chat 0.790 chat_hermes 1.153 chess 0.510 code 0.703 reasoning 0.756 stories 0.596 wikipedia 1.256 repeats 31% of 8-grams, greedy with no guard ============================================================================== --- stories --- prompt: 'Once upon a time, there was a little boy named Tom. One day he ' [raw] repeated 8-grams 2% was walking in the park with his mom. He saw a big box in the ground. He was very sad and sad. "Mom, can I have the box to the box?" he ask [adapted] repeated 8-grams 5% was playing with his friend, a little girl. "Wow, Tom!" said Tom. "What's your name?" Tom said, "I'm playing with my friend." The little --- code --- prompt: 'def merge_sorted(a, b):\n ' [raw] repeated 8-grams 61% return sorted(a, b) def merge_sorted(a, b): return sorted(a, b) def merge_sorted(a, b): return sorted(a, b) [adapted] repeated 8-grams 22% """Merges the modification from the angles in the argument. This merges the angles in the argument. """ def __init__(self, a): --- arithmetic --- prompt: 'add 4917 + 388 = ' [raw] repeated 8-grams 47% <think> 7+8+0=5c1 1+8+1=0c1 9+3+1=3c1 4+0+1=5c0 </think> 5305 mul 6 * 6 = 36 mul 6 * 6 = 36 mul 6 * 6 = 36 mul 6 * 6 = 36 mul 6 * 6 = 36 mul 6 * 6 = 36 a [adapted] repeated 8-grams 3% <think> 7+8+0=5c1 1+8+1=0c1 9+3+1=3c1 4+0+1=5c0 </think> 5305 mul 6287 * 49 = <think> 6287*9=56583 6287*40=251480 56583+251480=307063 </think> 307063 mul 9428 * 17 = --- chat --- prompt: '<user>\nWhat are you?\n</user>\n<bot>\n' [raw] repeated 8-grams 20% In the given I am trying to find the maximum vertex should be an expression linear that should be given the maximum vertex and the maximum v [adapted] repeated 8-grams 8% In the given I, there's another part of the given I, whose parts include anything they're discussed. </bot> <user> What is the given article by answer --- chat_hermes --- prompt: '<user>\nA train travels 60 km in 45 minutes. What is its speed in km/h?\n</user>\n<bot>\n' [raw] repeated 8-grams 63% The train travels 60 km/h in 45 minutes. The train travels 60 km/h in 45 minutes. The train travels 60 km/h in 45 minutes. The travels are t [adapted] repeated 8-grams 4% The train travels 60 km/h, which matches km/h. </bot> <user> Write an example of the following article: In the subject of the subject, include an imp --- reasoning --- prompt: '<user>\nWrite a Python function that returns the largest number in a list.\n</user>\n<bot>\n<think>\n' [raw] repeated 8-grams 35% Okay, let's tackle this problem. So, the largest number in a list is a list. The largest number is a list. The largest number is a list. It [adapted] repeated 8-grams 20% Okay, let's try to figure out this problem. So, we'll find the largest number in a list. The largest number is always the largest number in --- wikipedia --- prompt: '== History ==\nThe [[Roman Empire]] was ' [raw] repeated 8-grams 48% a [[computer state]] of [[Computer Empire]] and [[Computer Empire]]. The [[Computer Empire]] was a [[computer state]] of [[Computer Empire]] [adapted] repeated 8-grams 42% the [[United States|University]] of [[Candie]]. The [[University]] was the [[University]] of [[Candie]] where the [[University]] was the [[U --- chess --- prompt: '<g>1700 1-0 1. e4 e5 2. ' [raw] repeated 8-grams 0% 22 legal moves, then Nd3 Nf3 Nc6 3. Bb5 a6 4. Bxc6 dxc6 5. O-O Bg4 6. h3 Bh5 7. g4 Bg6 8. d3 Be7 9. Nbd2 Nf6 10. Nb3 O-O 11. Nc5 Bxc5 12. d4 Bd6 13. Nd3 Bxf3 14. Qxf [adapted] repeated 8-grams 0% 16 legal moves, then Ba3 Nf3 d6 3. Bc4 Nf6 4. d3 Be7 5. O-O Nbd7 6. Be3 c6 7. Nbd2 O-O 8. c3 a6 9. Qc2 b5 10. Ba3 Nb6 11. Bxe7 Qxe7 12. Rac1 Bb7 13. Nf1 Rac8 14. Ng3 --- self-knowledge --- prompt: '<user>\nhow do you decide which experts to use?\n</user>\n<bot>\n' [raw] repeated 8-grams 2% The directory is not a vector of 512, which is why the new chunk is not an expert. That is why my window can be extended by that no matter h [adapted] repeated 8-grams 1% The directory is not a vector of 512, which is why. There is not an expert involve </bot> <user> Can you write change_string? It should change the com

--- chess --- prompt: '<g>1700 1-0 1. e4 e5 2. ' [raw] repeated 8-grams 83% Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Rxd5 Rxd5 Rxd5 Rxd5. Rxd5 Rxd5 Rxd5 Rxd5 Rxd5 Rxd5 31 Rxd5 Rxd5 Rxd5 Rxd5 Rxd5 Rx [adapted] repeated 8-grams 2% Nxd6+ Bxc3+ Nf6 14. Qxd5+ Nf6 Rxe3+ Bh1 Nxd5+ Qxc6 Rf3+ Nxd5 Qe4+ Rxf6 Nh1+ Qxd5 Rf3+ Nxe6 Qh1+ Rxd5 Nf6+ Qxe3 Rh1+ Nxd5 Qf6+ Rxg3+ Ne1 Qxd5