Welcome to Chatbot AI General #121, the thread for discussing and improving AI pony chatbots.
▶ MLP Bots
https://mlpchag.neocities.org
Spreadsheet (CAI bots + Old repository):
https://docs.google.com/spreadsheets/d/1J7BeqJVDS51cXF8Pgm2YZaFq-Z6ykSJT
SillyTavern website extension: https://github.com/MLPChag/SillyTavern-Chag-Search
Bunch of Character Cards (Descriptions/No Greetings):
https://docs.google.com/spreadsheets/d/1Y6LNOCqAZAWIX-OBEv55HjlzcEdpUh-XbKCpbpL6v5k
CAI bots converted to Tavern: https://files.catbox.moe/ckurq1.zip
Expression packs: https://rentry.org/ChagExpressions
!!!GALLERY!!!: https://drive.google.com/drive/u/2/folders/1Ao-h5HFGMPllSrzSBKM_BvGSiU9f0c2U
▶ How do I start?
1) Select a Frontend
2) Select an AI model
3) Select Jailbreak
4) Select bots
5) Lovemaking with AI mares!
Starting in this hobby can be confusing and difficult. If it’s your first time and you’re lost,
▶ ASK THE THREAD! ◀
Novice-to-advanced guide: https://rentry.org/onrms
▶ SillyTavern (preferred frontend)
https://github.com/SillyTavern/SillyTavern
On Android: https://rentry.org/STAI-Termux
App that voices pony responses in ST: https://drive.google.com/drive/folders/16Ss26VBmgzcSuTGzhaHqRuyVRceTf-YB
▶ More frontends:
Risu: https://risuai.xyz
Agnai: https://agnai.chat
▶ Locals
https://rentry.org/lunarmodelexperiments
>>>/g/lmg/
Mistral Nemo base model fine-tuned on fimfics: https://huggingface.co/Ada321/Nemo_Pony_2/tree/main
▶ Jailbreaks
MLP JB: https://rentry.org/znon7vxe
More JB and guides: https://rentry.org/jb-listing
Hypebots for Tavern: https://rentry.org/pn3hb
▶ Botmaking
Editors: https://agnai.chat/editor
Guides: https://rentry.org/meta_botmaking_list
Advanced: https://rentry.org/AdvancedCardWritingTricks
▶ Miscellaneous
Chag Arts: https://drive.google.com/drive/u/2/folders/1yfajin_hB5rW_jy6WyFjRaRfDA3EUGiJ
https://rentry.org/ChagMiscellaneous
https://rentry.org/ChagArchive
Previous thread: >>42105276
**News:**
- Grok 3 (beta) released on API https://docs.x.ai/docs/models
- OAI? release new, free, test models on OpenRouter: Quasar Alpha and Optimus Alpha
- Chatgpt-4o-latest API updated to match frontend version; native image generation released https://openai.com/index/introducing-4o-image-generation
- Gemini 2.5 Pro Experimental released https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025/
- Deepseek V3 updated, new weights released https://huggingface.co/deepseek-ai/DeepSeek-V3-0324
/mlpol/ - My Little Politics
29 replies | 10 files | 22 UUIDs | Page 4
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Anchor for bots, lorebooks, scenarios.
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Anchor for technical stuffs (Proxies, Updates, Models etc.)
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Anchor for asking for bots, lorebooks, scenarios etc
Hello Anon, good to see that /CHAG/ lives on!
Now discovered a legitimate /CHAG/ on nhnb. But let it be this one.
https://nhnb.org/fim/res/20712.html
https://nhnb.org/fim/res/20712.html
>>383739
I like it here, the nhnb captcha is pretty broken for me, i am forced to resolve it at least five times until my post gets through.
I like it here, the nhnb captcha is pretty broken for me, i am forced to resolve it at least five times until my post gets through.
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>>383752
There is a toggle-able captcha system here, in the event of raiders (one of the reasons soyjack doesn't like to play with us anymore :3), but by default it's turned off.
Weird, almost like we want our posters to feel welcome and shit.
There is a toggle-able captcha system here, in the event of raiders (one of the reasons soyjack doesn't like to play with us anymore :3), but by default it's turned off.
Weird, almost like we want our posters to feel welcome and shit.
Kek. There's one on mlpol, Poner, and NHNB too.
The NHNB one seems to be the most active for now.
The NHNB one seems to be the most active for now.
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>/CHAG/ has migrated
Excellent.
Excellent.
Sorry anons, the CHAG circlejerk shadow cabal secret party has chosen NHNB.
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It's alive
Are there any models that run locally? I heard deepseek has models that can run locally with 16 gigs of ram.
>>384447
>Are there any models that run locally?
Yeah, plenty, but what you can run depends heavily on your VRAM.
Text generation models require a lot of it, way more than image generation, for example.
They'll also be inferior to the big models in terms of intelligence, instruction-following, and pony knowledge.
>I heard Deepseek has models that can run locally with 16GB of RAM.
Unfortunately, that claim is mostly clickbait.
When people talk about Deepseek, they're usually referring to one of two models:
>Deepseek R1
An open-source "thinking" model. This is the one that made them well-known.
It's technically open-source, but you're not running that locally, unless you're sitting on something absurd.
Even with quantization, you'd probably need 200–300GB of VRAM or RAM to run it.
Not sure about the calcul, but the short version: you're not running it on a normal consumer machine.
>Deepseek V3
Also a large model, but not a "thinking" one.
Same deal, you're not running this locally either.
The "Deepseek" models people say can run locally aren't really Deepseek models.
They're smaller local models that replicated R1's "thinking" approach and core ideas, but they aren't the same thing.
So if you hear someone say they're running "Deepseek" on a Raspberry Pi, it's actually just some janky 1.5B model that mimics the concept but not the performance.
From what I've heard, those versions aren't great anyway, but I haven't tested them myself.
If you want to try the real R1 or V3, you can use free OpenRouter keys, but obviously, that's not local.
>Are there any models that run locally?
Yeah, plenty, but what you can run depends heavily on your VRAM.
Text generation models require a lot of it, way more than image generation, for example.
They'll also be inferior to the big models in terms of intelligence, instruction-following, and pony knowledge.
>I heard Deepseek has models that can run locally with 16GB of RAM.
Unfortunately, that claim is mostly clickbait.
When people talk about Deepseek, they're usually referring to one of two models:
>Deepseek R1
An open-source "thinking" model. This is the one that made them well-known.
It's technically open-source, but you're not running that locally, unless you're sitting on something absurd.
Even with quantization, you'd probably need 200–300GB of VRAM or RAM to run it.
Not sure about the calcul, but the short version: you're not running it on a normal consumer machine.
>Deepseek V3
Also a large model, but not a "thinking" one.
Same deal, you're not running this locally either.
The "Deepseek" models people say can run locally aren't really Deepseek models.
They're smaller local models that replicated R1's "thinking" approach and core ideas, but they aren't the same thing.
So if you hear someone say they're running "Deepseek" on a Raspberry Pi, it's actually just some janky 1.5B model that mimics the concept but not the performance.
From what I've heard, those versions aren't great anyway, but I haven't tested them myself.
If you want to try the real R1 or V3, you can use free OpenRouter keys, but obviously, that's not local.
>>384481
I can run R1 locally on the Q2_K_XL dynamic quant on 256 gigabytes octochannel ddr4 ram but I also have 4x3090 to offload some of it to GPU and that's good for about 2 tokens per second on a good day. But yeah you have to really be into this stuff to even bother.
I can run R1 locally on the Q2_K_XL dynamic quant on 256 gigabytes octochannel ddr4 ram but I also have 4x3090 to offload some of it to GPU and that's good for about 2 tokens per second on a good day. But yeah you have to really be into this stuff to even bother.
>>384481
your description of quantization is wrong. quantitized models are directly based off of the original model and often made by the same people, but the precision of the data is lower, ie reducing from FP32 to FP16 or FP8.
your description of quantization is wrong. quantitized models are directly based off of the original model and often made by the same people, but the precision of the data is lower, ie reducing from FP32 to FP16 or FP8.
>>384531
Quantization is the process of either naively or selectively reducing the bit width of the parameters of a model after it has already been trained in order to lower the memory footprint. They aren't really "based off" the original model. They ARE the original model with some of its brains scooped out. It's different than, say, training the model in a lower precision to begin with. For example DeepSeek is trained natively in FP8 afaik. Most really big LLMs get trained in FP8 these days because, generally speaking, the finding is that quantizing an LLM down to 8.0bpw (bits per weight) is generally considered to be lossless. (It's not, but it's damn close in most cases, although directly comparing quantization error of one model to a different model isn't really fair). The quantization error doesn't REALLY start stacking up until you go below 4bpw. Although oddly enough selective quants of R1 still perform very well as low as 1.58bpw. I theorize, personally, because A. it's a MoE which have naturally high data sparsity, B. because it was trained in native FP8, so theoretically at 2bpw it's 'only' lost 75% of its initial data, which is the same ratio of reduction as an FP16 model quanted down to 4 bpw.
My general finding with quantization is that, for the most part a quantized model still has most of its 'knowledge' intact. But quantization error manifests more.. It's hard to explain... Early naive 4-bit quantizations had a tendency to do things like reverse possessive clauses. Basically small, semantically close linguistic concepts get muddled with deep quantization while the overall 'world knowledge' of the model remains intact. It's a pseudoscientific claim on my part, working entirely from experience but given that there's more possible mathematical configurations of a model's parameters than there are particles in the universe 10^100000 times over you aren't going to get a hard scientific explanation of that one any time soon.
Although we are far from the "hyperfitting" wall. I would assume as we get closer to that quantization will become more destructive. TLDR of hyperfitting is that currently models are far from being trained to the point that they can no longer form usable information. But it takes months to do the pretraining on the models we do have so hyperfitting larger models (versus small lab test models) would probably take years and nobody wants to start training a model that might be architecturally obsolete by the time its done.
Quantization is the process of either naively or selectively reducing the bit width of the parameters of a model after it has already been trained in order to lower the memory footprint. They aren't really "based off" the original model. They ARE the original model with some of its brains scooped out. It's different than, say, training the model in a lower precision to begin with. For example DeepSeek is trained natively in FP8 afaik. Most really big LLMs get trained in FP8 these days because, generally speaking, the finding is that quantizing an LLM down to 8.0bpw (bits per weight) is generally considered to be lossless. (It's not, but it's damn close in most cases, although directly comparing quantization error of one model to a different model isn't really fair). The quantization error doesn't REALLY start stacking up until you go below 4bpw. Although oddly enough selective quants of R1 still perform very well as low as 1.58bpw. I theorize, personally, because A. it's a MoE which have naturally high data sparsity, B. because it was trained in native FP8, so theoretically at 2bpw it's 'only' lost 75% of its initial data, which is the same ratio of reduction as an FP16 model quanted down to 4 bpw.
My general finding with quantization is that, for the most part a quantized model still has most of its 'knowledge' intact. But quantization error manifests more.. It's hard to explain... Early naive 4-bit quantizations had a tendency to do things like reverse possessive clauses. Basically small, semantically close linguistic concepts get muddled with deep quantization while the overall 'world knowledge' of the model remains intact. It's a pseudoscientific claim on my part, working entirely from experience but given that there's more possible mathematical configurations of a model's parameters than there are particles in the universe 10^100000 times over you aren't going to get a hard scientific explanation of that one any time soon.
Although we are far from the "hyperfitting" wall. I would assume as we get closer to that quantization will become more destructive. TLDR of hyperfitting is that currently models are far from being trained to the point that they can no longer form usable information. But it takes months to do the pretraining on the models we do have so hyperfitting larger models (versus small lab test models) would probably take years and nobody wants to start training a model that might be architecturally obsolete by the time its done.
Is this finally the day I can be the first one to say "Lovemaking with AI mares"? I hope so.
