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i64systemsΒ 
posted an update 3 days ago
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we taught a small model to predict how simulated fluid would move eight steps into the future. it learned from examples, then succeeded on examples withheld from training.
two things mattered:
- its memory helped. the version with an internal memory produced about 41–45% less prediction error than the comparison model using recent observations.
- the learning was repeatable. restarting training reproduced every recorded update and saved checkpoint exactly. resuming halfway through also produced the same ending.
that gives us working evidence that bf16 gpu training can learn and remain exactly repeatable on this 3090 ti and software setup.

thank you for your time.
i64systemsΒ 
posted an update 4 days ago
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little bob’s updated weights now change his actions in doom. all three fresh replays matched exactly

yes i am training my bf16/fp32 non-language models weights with replayable inference *in doom*

Hoglet-33Β 
updated a Space 5 days ago
Hoglet-33Β 
posted an update 5 days ago
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3155
Today, we planned to release Pebble-50M and Pebble-50M-Chat to the world. Unfortunately, due to a few issues, that didn't go quite as planned.

What happened:
- Some data and benchmark results were lost or corrupted
- The models performed worse on benchmarks than our other Pebble models

Despite that, you can still find both models here:
Pebble-50M-beta: basically-experimental/Pebble-50M-beta
Pebble-50M-Chat-beta: basically-experimental/Pebble-50M-Chat-beta

There are still some interesting improvements in these models:
- Compatible with non-CUDA devices
- Vocabulary increased to 16K tokens
- Context length increased to 16K tokens

For now, there won't be any more Pebble releases for a while. We're going to take some time to experiment with other approaches and hopefully make the next generation a monumental leap over this one.

Follow for updates:
@Hoglet-33
basically-ai

basically-experimental
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i64systemsΒ 
posted an update 7 days ago
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NOW UPDATED ❗❗

openbob 5.0.1πŸ­πŸ”Ž

An integer execution method for reproducible inference from publicly available model weights, demonstrated on Qwen3-4B. Journaled bytes and all.

Keep an eye out for the gpt-oss-120B on the 24gb GPU- deterministically.
We make AI models do the same things every time!β›“οΈπŸ˜ˆ
i64systems/Qwen3-4B-openbob-i8
Hoglet-33Β 
posted an update 9 days ago
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3437
Pebble-25M and Pebble-25M-Chat are out now!

We’re excited to release Pebble-25M and Pebble-25M-Chat!

Both models use our 3:1 Mamba2/Transformer hybrid architecture and were pretrained on 25B tokens. Pebble-25M-Chat was then further fine-tuned on an additional 250M tokens from smol-smoltalk, following the same approach used for the Pebble-10M models.

We hope you enjoy experimenting with them!

Pebble-50M is coming in a few days.

Models
Pebble-25M: basically-ai/Pebble-25M
Pebble-25M-Chat: basically-ai/Pebble-25M-Chat
Pebble-10M GGUFs

In case you missed it, our friend @ContextReq made GGUF versions of the Pebble-10M models:

https://huggingface.co/ContextReq/Pebble-10M-GGUF
https://huggingface.co/ContextReq/Pebble-10M-Chat-GGUF

Follow us if you don’t want to miss future releases and updates!

@Hoglet-33
basically-ai

basically-experimental
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i64systemsΒ 
posted an update 9 days ago
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remat is no longer a one-model claim!!proved it on Qwen3-30B-A3B, K=32 of 128 experts resident, output task byte-identical to the full reference, zero bytes different *in bf16*πŸ₯°πŸ₯° GPU comes next😈

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Hoglet-33Β 
published a Space 11 days ago
Hoglet-33Β 
posted an update 12 days ago
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Pebble 10M and Pebble 10M Chat are now released!

Both models use our Mamba/Transformer 3:1 hybrid architecture and were pretrained on 25 billion tokens.

Pebble 10M Chat was additionally fine-tuned on 250 million tokens of Smol-SmolTalk to improve its conversational capabilities.

You can find them here:

- basically-ai/Pebble-10M
- basically-ai/Pebble-10M-Chat

We hope you enjoy using them. The rest of the Pebble family will be released soon.

Follow for more:
@Hoglet-33
basically-ai
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Hoglet-33Β 
posted an update 16 days ago
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We are announcing the first generation of the Pebble model family!

These are the models we are releasing:

- Pebble 10M
- Pebble 25M
- Pebble 50M

Each model will use a Mamba-Transformer 3:1 hybrid architecture and will be pretrained on 25 billion tokens before IFT and SFT.

Depending on development time and resources, we may also release:

- Pebble 5M
- Pebble 75M
- Pebble 1M (possibly)

We hope you're excited and enjoy the models!

Follow for more:
@Hoglet-33
basically-ai
  • 7 replies
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