55 26 383

Pablo Carrera (パブロ) PRO

pabloce

AI & ML interests

Hello, hello!

Recent Activity

reacted to cgeorgiaw's post with ❤️ 2 days ago

Huge new bio datasets just dropped!!! Check out them out @ https://huggingface.co/ginkgo-datapoints Read the blog for more info: https://huggingface.co/blog/cgeorgiaw/gdp

reacted to bartowski's post with 🤗 4 days ago

Was going to post this on /r/LocalLLaMa, but apparently it's without moderation at this time :') https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF Was able to use previous mistral chat templates, some hints from Qwen templates, and Claude to piece together a seemingly working chat template, tested it with llama.cpp server and got perfect results, though lmstudio still seems to be struggling for some reason (don't know how to specify a jinja file there) Outlined the details of the script and results in my llama.cpp PR to add the jinja template: https://github.com/ggml-org/llama.cpp/pull/14349 Start server with a command like this: ``` ./llama-server -m /models/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf --jinja --chat-template-file /models/Mistral-Small-3.2-24B-Instruct-2506.jinja ``` and it should be perfect! Hoping it'll work for ALL tools if lmstudio gets an update or something, not just llama.cpp, but very happy to see it works flawlessly in llama.cpp In the meantime, will try to open a PR to minja to make the strftime work, but no promises :)

liked a model 7 days ago

mistralai/Mistral-Small-3.2-24B-Instruct-2506

View all activity

Organizations

reacted to cgeorgiaw's post with ❤️ 2 days ago

Post

2396

Huge new bio datasets just dropped!!!

Check out them out @

ginkgo-datapoints
Read the blog for more info: https://huggingface.co/blog/cgeorgiaw/gdp

reacted to bartowski's post with 🤗 4 days ago

Post

5191

Was going to post this on /r/LocalLLaMa, but apparently it's without moderation at this time :')

bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF

Was able to use previous mistral chat templates, some hints from Qwen templates, and Claude to piece together a seemingly working chat template, tested it with llama.cpp server and got perfect results, though lmstudio still seems to be struggling for some reason (don't know how to specify a jinja file there)

Outlined the details of the script and results in my llama.cpp PR to add the jinja template:

https://github.com/ggml-org/llama.cpp/pull/14349

Start server with a command like this:

./llama-server -m /models/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf --jinja --chat-template-file /models/Mistral-Small-3.2-24B-Instruct-2506.jinja

and it should be perfect! Hoping it'll work for ALL tools if lmstudio gets an update or something, not just llama.cpp, but very happy to see it works flawlessly in llama.cpp

In the meantime, will try to open a PR to minja to make the strftime work, but no promises :)

liked a model 7 days ago

mistralai/Mistral-Small-3.2-24B-Instruct-2506

Image-Text-to-Text • 24B • Updated 5 days ago • 19.2k • 270

liked 3 models 11 days ago

updated a model 13 days ago

somosnlp-hackathon-2025/mistral-7b-gastronomia-hispana-qlora-GGUF

7B • Updated 13 days ago • 88

published a model 13 days ago

somosnlp-hackathon-2025/mistral-7b-gastronomia-hispana-qlora-GGUF

7B • Updated 13 days ago • 88

updated a Space 14 days ago

Exllama

😽

Chat: exllama v2

upvoted an article 16 days ago

Article

Post-Training Isaac GR00T N1.5 for LeRobot SO-101 Arm

and 4 others •

16 days ago

• 63

reacted to danielhanchen's post with 🔥 16 days ago

Post

1887

Mistral releases Magistral, their new reasoning models! 🔥
GGUFs to run: unsloth/Magistral-Small-2506-GGUF

Magistral-Small-2506 excels at mathematics and coding.

You can run the 24B model locally with just 32GB RAM by using our Dynamic GGUFs.

reacted to cbensimon's post with 🔥🤗❤️🚀 16 days ago

Post

3092

🚀 ZeroGPU now supports PyTorch native quantization via torchao

While it hasn’t been battle-tested yet, Int8WeightOnlyConfig is already working flawlessly in our tests.

Let us know if you run into any issues — and we’re excited to see what the community will build!

import spaces
from diffusers import FluxPipeline
from torchao.quantization.quant_api import Int8WeightOnlyConfig, quantize_

pipeline = FluxPipeline.from_pretrained(...).to('cuda')
quantize_(pipeline.transformer, Int8WeightOnlyConfig()) # Or any other component(s)

@spaces.GPU
def generate(prompt: str):
    return pipeline(prompt).images[0]