Jackrong/Qwen3.5-2B-Claude-4.6-Opus-Reasoning-Distilled-GGUF
Text Generation • 2B • Updated • 73.5k • 147
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Just thank you.
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#1 opened about 1 month ago
by Nopanolator
liked 2
models about 1 month ago
upvoted an article about 2 months ago
Article
GGML and llama.cpp join HF to ensure the long-term progress of Local AI
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501
Thanks, a lot.
2
#7 opened 4 months ago
by Nopanolator
liked a
model 4 months ago
People who vote in your polls have no idea wtf they talk about
👍 9
4
#3 opened 6 months ago
by 
SerialKicked
reactedto martinsu's post with 🔥 4 months ago
Post
2046
I wasted days on a GPU node on a bug that shouldn't exist
So I was fine-tuning TildeOPEN-30B and the outputs were... weird. Token ID 179 (<0x00>) kept appearing between almost every token pair. Took me a bit to figure out what was going on.
Turns out I used the fast tokenizer for training, but the model was trained on the slow one. Silent failure.
Well... long story short—TGI uses (forces) the fast tokenizer, no questions asked. And you'll have agile's kryptonite: silent failure. If the model was trained on slow, it's a silent disaster.
I got curious and wrote a quick script to check how common this is. Ran it on 6,014 LLM HF models overnight.
Roughly 10% of HF model downloads have mismatched tokenizers. Not all mismatches are catastrophic, but some are brutal — like chat template markers inflating from 1 token to 3, silently wrecking context windows and causing model act weird.
This wasn't rigorous research, but the drift is real. And the worst part? 968 models(out of 500+ downloads) have both fast and slow tokenizers present, but they still produce different outputs. No missing files, no errors — just silent degradation.
TGI defaults to the fast tokenizer, as does AutoTokenizer.from_pretrained(). If a fast tokenizer doesn't exist, it auto-generates one. If your model was trained on slow, you get silent degradation. Output looks fine; the model just performs worse. Sometimes really worse. You'd never know.
If model was trained on fast tokenizer, its fine, but how do You know?
The root cause? Either model authors run HF conversion and upload both without verifying, or users run TGI, which always forces(converts to) fast .
The result of this fight with tokenizers is martinsu/tildeopen-30b-mu-instruct
It's based on TildeOPEN-30B (a solid EU HPC multilingual base). Nothing fancy—just a proper instruction fine-tune where I didn't mess up the tokenizer this time.
Full article: https://github.com/martins-u/tokenmagedon
So I was fine-tuning TildeOPEN-30B and the outputs were... weird. Token ID 179 (<0x00>) kept appearing between almost every token pair. Took me a bit to figure out what was going on.
Turns out I used the fast tokenizer for training, but the model was trained on the slow one. Silent failure.
Well... long story short—TGI uses (forces) the fast tokenizer, no questions asked. And you'll have agile's kryptonite: silent failure. If the model was trained on slow, it's a silent disaster.
I got curious and wrote a quick script to check how common this is. Ran it on 6,014 LLM HF models overnight.
Roughly 10% of HF model downloads have mismatched tokenizers. Not all mismatches are catastrophic, but some are brutal — like chat template markers inflating from 1 token to 3, silently wrecking context windows and causing model act weird.
This wasn't rigorous research, but the drift is real. And the worst part? 968 models(out of 500+ downloads) have both fast and slow tokenizers present, but they still produce different outputs. No missing files, no errors — just silent degradation.
TGI defaults to the fast tokenizer, as does AutoTokenizer.from_pretrained(). If a fast tokenizer doesn't exist, it auto-generates one. If your model was trained on slow, you get silent degradation. Output looks fine; the model just performs worse. Sometimes really worse. You'd never know.
If model was trained on fast tokenizer, its fine, but how do You know?
The root cause? Either model authors run HF conversion and upload both without verifying, or users run TGI, which always forces(converts to) fast .
The result of this fight with tokenizers is martinsu/tildeopen-30b-mu-instruct
It's based on TildeOPEN-30B (a solid EU HPC multilingual base). Nothing fancy—just a proper instruction fine-tune where I didn't mess up the tokenizer this time.
Full article: https://github.com/martins-u/tokenmagedon
- 1 reply
Discovering heretic on this release : nice
1
#5 opened 4 months ago
by Nopanolator
Bottle in the sea
#1 opened 4 months ago
by Nopanolator
is this a joke? "funny" meta commentary?
😔 1
4
#2 opened 5 months ago
by 
MikaSouthworth
liked 3
models 5 months ago
reactedto cjerzak's post with 👀 5 months ago
Post
2893
>>> We're writing a new book, <Planetary Causal Inference>, on how to model counterfactuals at planetary scale by combining satellite imagery + other global data with local studies and RCTs. Forthcoming in 2026+.
>>> Book info: https://planetarycausalinference.org/book-launch
>>> All datasets used in the book will be openly available on our lab’s Hugging Face hub:
theaidevlab
>>> Book info: https://planetarycausalinference.org/book-launch
>>> All datasets used in the book will be openly available on our lab’s Hugging Face hub: