Pleias-RAG
Collection
New generation of small reasoning models for RAG, search, and source summarization.
•
4 items
•
Updated
•
22
Shawon Ashraf
shawon
AI & ML interests
Multi-Modal NLP, LLM and RAG
Recent Activity
upvoted
a
collection
2 days ago
Pleias-RAG
liked
a model
3 days ago
nvidia/DAM-3B
upvoted
a
collection
3 days ago
Describe Anything
Organizations
shawon's activity
upvoted
a
collection
2 days ago
liked
a
model
3 days ago
upvoted
a
collection
3 days ago
liked
a
model
3 days ago
liked
3
datasets
6 days ago
liked
a
dataset
11 days ago
liked
a
model
13 days ago
liked
a
model
14 days ago
reacted to
AdinaY's
post with 🔥
14 days ago
Post
3183
Shanghai AI Lab - OpenGV team just released InternVL3 🔥
OpenGVLab/internvl3-67f7f690be79c2fe9d74fe9d
✨ 1/2/8/9/14/38/28B with MIT license
✨ Stronger perception & reasoning vs InternVL 2.5
✨ Native Multimodal Pre-Training for even better language performance
OpenGVLab/internvl3-67f7f690be79c2fe9d74fe9d
✨ 1/2/8/9/14/38/28B with MIT license
✨ Stronger perception & reasoning vs InternVL 2.5
✨ Native Multimodal Pre-Training for even better language performance
- 1 reply
reacted to
etemiz's
post with 👀
15 days ago
Post
2169
It looks like Llama 4 team gamed the LMArena benchmarks by making their Maverick model output emojis, longer responses and ultra high enthusiasm! Is that ethical or not? They could certainly do a better job by working with teams like llama.cpp, just like Qwen team did with Qwen 3 before releasing the model.
In 2024 I started playing with LLMs just before the release of Llama 3. I think Meta contributed a lot to this field and still contributing. Most LLM fine tuning tools are based on their models and also the inference tool llama.cpp has their name on it. The Llama 4 is fast and maybe not the greatest in real performance but still deserves respect. But my enthusiasm towards Llama models is probably because they rank highest on my AHA Leaderboard:
https://sheet.zoho.com/sheet/open/mz41j09cc640a29ba47729fed784a263c1d08
Looks like they did a worse job compared to Llama 3.1 this time. Llama 3.1 has been on top for a while.
Ranking high on my leaderboard is not correlated to technological progress or parameter size. In fact if LLM training is getting away from human alignment thanks to synthetic datasets or something else (?), it could be easily inversely correlated to technological progress. It seems there is a correlation regarding the location of the builders (in the West or East). Western models are ranking higher. This has become more visible as the leaderboard progressed, in the past there was less correlation. And Europeans seem to be in the middle!
Whether you like positive vibes from AI or not, maybe the times are getting closer where humans may be susceptible to being gamed by an AI? What do you think?
In 2024 I started playing with LLMs just before the release of Llama 3. I think Meta contributed a lot to this field and still contributing. Most LLM fine tuning tools are based on their models and also the inference tool llama.cpp has their name on it. The Llama 4 is fast and maybe not the greatest in real performance but still deserves respect. But my enthusiasm towards Llama models is probably because they rank highest on my AHA Leaderboard:
https://sheet.zoho.com/sheet/open/mz41j09cc640a29ba47729fed784a263c1d08
Looks like they did a worse job compared to Llama 3.1 this time. Llama 3.1 has been on top for a while.
Ranking high on my leaderboard is not correlated to technological progress or parameter size. In fact if LLM training is getting away from human alignment thanks to synthetic datasets or something else (?), it could be easily inversely correlated to technological progress. It seems there is a correlation regarding the location of the builders (in the West or East). Western models are ranking higher. This has become more visible as the leaderboard progressed, in the past there was less correlation. And Europeans seem to be in the middle!
Whether you like positive vibes from AI or not, maybe the times are getting closer where humans may be susceptible to being gamed by an AI? What do you think?
- 4 replies
liked
a
Space
15 days ago
updated
a
model
15 days ago
published
a
model
15 days ago
liked
a
model
16 days ago
reacted to
danielhanchen's
post with 🤗
18 days ago
Post
4710
You can now run Llama 4 on your own local device! 🦙
Run our Dynamic 1.78-bit and 2.71-bit Llama 4 GGUFs:
unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF
You can run them on llama.cpp and other inference engines. See our guide here: https://docs.unsloth.ai/basics/tutorial-how-to-run-and-fine-tune-llama-4
Run our Dynamic 1.78-bit and 2.71-bit Llama 4 GGUFs:
unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF
You can run them on llama.cpp and other inference engines. See our guide here: https://docs.unsloth.ai/basics/tutorial-how-to-run-and-fine-tune-llama-4
-
- 1 reply
liked
a
dataset
18 days ago
authored
a
paper
18 days ago
liked
a
model
18 days ago
