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Undi95 
posted an update 1 day ago
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2415
Hi!

I will get off the internet for a moment.
I launched the Hanami Project because I didn't supported SillyTavern UI anymore atm.
Too much options for my dead brain, still very good, but I wanted more simple, professional, phone accessible and sober front end for when I will be gone from home.

I did my maximum to finish it before I go, I want you to have it, I want my work to be used (even if it's AI slop for some of you) for who care.

Here's the github repo: https://github.com/Undi95/Hanami

If you have any suggestion, bugs report, pull request or anything, post it, if you want to modify it, fork it, but keep the credit, and add myself haha.

If you search an option, a function, you will find it. But at first, the front end will be what you expect: minimalist, but customizable, empty at first. Navigate to see all it can do. Everything is well organized.

Context is full ? No worries anymore, with memory file, files access, auto compaction and smooth transition, you can continue your chat like nothing happened. (Inspired from Claude)

The front end have a final option for everyone : The tools calling for action and emotion could be a bit too much for smaller model, you can, in this case, use the "Simple" option in Settings > Model > Model mode.

"Simple: no tools are exposed to the model — Hanami handles memory server-side (facts are extracted during compaction) and guesses the emotion from the text. Pick this for small models, which often fail at tool calling."

My last gift for myself, and for you.
Cya!
grimjim 
posted an update 6 months ago
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1371
After tinkering with Gemma Scope 2, I now have an mechanistic explanation of why Winsorization was as effective as it was in my ablation experiments on Gemma 3 12B Instruct. In short, the activation for the BOS token overwhelms everything else. Gemma Scope 2 deliberately did not train on the BOS token. Winsorization capped the magnitude of the BOS token, allowing the activations of other tokens to be compared.
google/gemma-scope-2-12b-it

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grimjim 
posted an update 6 months ago
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576
The contrarian in me is wary of the irrational exuberance over MoltBook. Nothing so far has struck me as being unpredictable. We knew already that LLMs were good at roleplay, to the point where some users started to think of their chatbots as soulmates (only to lament when the underlying model was pulled), and that chatbots can fall into conversational basins when even two instances are allowed to chat with each other at length. The appearance of memes that postdate training cutoff is suspect, which implies at the very least that humans have injected something at the level of prompts or content/context to introduce them into conversation like a Chekhov's Gun. And we know that security holes are common in vibe coding, attended or not.
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grimjim 
posted an update 8 months ago
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3462
I wanted to call attention to Arli Ai's success in applying my recent modifications to refusal ablation to a MoE model successfully. Nice work, @OwenArli !
ArliAI/GLM-4.5-Air-Derestricted
Ablation on a MoE model is no small thing; I expect preserving norms/magnitudes during intervention better respects routing compared to naive refusal ablation.

(I would have tagged their org earlier, but that feature seemed to be broken via "@")

ArliAI
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grimjim 
posted an update 9 months ago
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3539
Going forward, I will be adopting the term Magnitude-Preserving Orthogonal Ablation (MPOA) for my recent work in mitigating model damage from abliteration. The technique potentially unlocks reasoning capacity previously occupied with safety refusal processing.

For details, start here: https://huggingface.co/blog/grimjim/norm-preserving-biprojected-abliteration

Showcase results: grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated (outperforms base instruct on UGI Leaderboard NatInt)

(The existing name, while technically accurate, was a bit of a mouthful.)
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grimjim 
posted an update 9 months ago
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5099
Implemented a proof of concept sampler in pure PyTorch and transformers.

Max P consists of a dynamic token filter which applies Winsorization to cap the probabilties of top tokens. Specifically, a base probability in the range of [0,1] is used to cap individual token probability; the sampler then redistributes excess proportionally.

https://github.com/jim-plus/maxp-sampler-poc

Combined with Temperature and Min P, this could represent a more intuitive way of reducing repetition in text generation.
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grimjim 
posted an update 10 months ago
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837
I've uploaded abliteration code with support for sparsification of the refusal vector. It's poorly documented, but the code should be straightforward.
https://github.com/jim-plus/llm-abliteration
The code is built atop a fork that enabled abliteration to be performed on models loaded in 4-bit or 8-bit bitsandbytes quantization. TransformerLens is not required, just plain Transformers. For those previously unaware, this opens up abliteration experimentation to more people with local VRAM limitations.

Since performing abliteration on a quant involves precision and perplexity loss, it stands to reason that a small amount of magnitude sparsification could filter out some noise and possibly even reduce the damage inflicted on latent space via ablation of the refusal vector.

There's a small but real acceleration of ablation of the refusal vector by reducing outer product operations from O(d²×n) to O(d×n), and then by pushing said computation layerwise to GPU. The code is hardcoded for CUDA acceleration currently. Normalization of the refusal vector was deferred in order to allow sparsification. In principle other behavior vector interventions could also be added and explored.
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Recommended Parameters?

4
#10 opened 12 months ago by
Maelle23
grimjim 
posted an update over 1 year ago
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2390
I recently have been looking at a paper titled "Why Warmup the Learning Rate? Underlying Mechanisms and Improvements", by Dayal Singh Kalra and Maissam Barkeshli, and was struck by "warmup" being analogous to simulated annealing.
https://arxiv.org/abs/2406.09405
Taking the physical analogy further, the "warmup" is a stochastic process to knock the system out of current local minima, allowing easier transition toward newer minima. It works because it reduces "fit" and therefore "friction".
lucyknada 
in anthracite-org/README over 1 year ago

knowledge and terminology

1
#1 opened over 1 year ago by
Markobes

8.0bpw?

1
#3 opened over 1 year ago by
svippixel

8.0bpw?

1
#3 opened over 1 year ago by
svippixel
Undi95 
posted an update over 1 year ago
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16174
Hi there!

If you want to create your own thinking model or do a better MistralThinker, I just uploaded my entire dataset made on Deepseek R1 and the axolotl config. (well I made them public)

Axolotl config : Undi95/MistralThinker-v1.1

The dataset : Undi95/R1-RP-ShareGPT3

You can also read all I did on those two discord screenshot from two days ago, I'm a little lazy to rewrite all kek.

Hope you will use them!
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License

4
#2 opened over 1 year ago by
mrfakename
grimjim 
posted an update over 1 year ago
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2468
This recent paper points to an explanation for the unreasonable effectiveness of Frankenmerges: Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach (2502.05171)

Specifically, the duplication of layers in Frankenmerges serves a purpose similar to what occurs in their recurrent-depth architecture. Successful frankenmerges that operate without additional fine-tuning are able to recover or "heal" from any damage due to abrupt transitions between layer blocks. Operational replicated layer blocks can provide functional benefits grounded in latent reasoning. Frankenmerges can also result in hybrid reasoning, by splicing together the latent reasoning of different models.

Back in April 2024, I was able to duplicate a few layers in the Llama 3 8B model, turning it into a 9B model, without harming benchmarks significantly, despite any transition damage.
grimjim/llama-3-experiment-v1-9B
My informal experimentation suggested that latent reasoning circuits could occupy continguous stacks of 2-4 layers, though the result was highly sensitive to the choice of transition location between layers.
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