Qwen Image 2512
👀
315
Generate high‑quality images from detailed text prompts
Yair Korin
yakito
AI & ML interests
None yet
Recent Activity
liked
a Space about 2 months ago
Qwen/Qwen-Image-2512 liked
a model about 2 months ago
Qwen/Qwen-Image-Layered liked
a Space about 2 months ago
Heartsync/NSFW-Uncensored-video2 Organizations
liked a Space about 2 months ago
liked a model about 2 months ago
liked 2 Spaces about 2 months ago
liked 2 Spaces 7 months ago
liked 4 Spaces 10 months ago
liked 3 Spaces 11 months ago
liked a model 11 months ago
reacted to AtAndDev's post with 😔 11 months ago
Post
4378
There seems to multiple paid apps shared here that are based on models on hf, but some ppl sell their wrappers as "products" and promote them here. For a long time, hf was the best and only platform to do oss model stuff but with the recent AI website builders anyone can create a product (really crappy ones btw) and try to sell it with no contribution to oss stuff. Please dont do this, or try finetuning the models you use...
Sorry for filling yall feed with this bs but yk...
Sorry for filling yall feed with this bs but yk...
- 6 replies
reacted to mlabonne's post with 🚀 11 months ago
Post
6584
✂️ Gemma 3 Abliterated
I noticed that Gemma 3 was much more resilient to refusal removal than other models like Qwen 2.5.
I experimented with different recipes and improved the abliteration technique I wrote about last year.
It's still experimental but the refusal rate is super low in my tests. Enjoy!
mlabonne/gemma-3-4b-it-abliterated
mlabonne/gemma-3-12b-it-abliterated
mlabonne/gemma-3-27b-it-abliterated
I noticed that Gemma 3 was much more resilient to refusal removal than other models like Qwen 2.5.
I experimented with different recipes and improved the abliteration technique I wrote about last year.
It's still experimental but the refusal rate is super low in my tests. Enjoy!
mlabonne/gemma-3-4b-it-abliterated
mlabonne/gemma-3-12b-it-abliterated
mlabonne/gemma-3-27b-it-abliterated
-
- 4 replies
reacted to Kseniase's post with 🔥 11 months ago
Post
8137
15 types of attention mechanisms
Attention mechanisms allow models to dynamically focus on specific parts of their input when performing tasks. In our recent article, we discussed Multi-Head Latent Attention (MLA) in detail and now it's time to summarize other existing types of attention.
Here is a list of 15 types of attention mechanisms used in AI models:
1. Soft attention (Deterministic attention) -> Neural Machine Translation by Jointly Learning to Align and Translate (1409.0473)
Assigns a continuous weight distribution over all parts of the input. It produces a weighted sum of the input using attention weights that sum to 1.
2. Hard attention (Stochastic attention) -> Effective Approaches to Attention-based Neural Machine Translation (1508.04025)
Makes a discrete selection of some part of the input to focus on at each step, rather than attending to everything.
3. Self-attention -> Attention Is All You Need (1706.03762)
Each element in the sequence "looks" at other elements and "decides" how much to borrow from each of them for its new representation.
4. Cross-Attention (Encoder-Decoder attention) -> Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation (2104.08771)
The queries come from one sequence and the keys/values come from another sequence. It allows a model to combine information from two different sources.
5. Multi-Head Attention (MHA) -> Attention Is All You Need (1706.03762)
Multiple attention “heads” are run in parallel. The model computes several attention distributions (heads), each with its own set of learned projections of queries, keys, and values.
6. Multi-Head Latent Attention (MLA) -> DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model (2405.04434)
Extends MHA by incorporating a latent space where attention heads can dynamically learn different latent factors or representations.
7. Memory-Based attention -> End-To-End Memory Networks (1503.08895)
Involves an external memory and uses attention to read from and write to this memory.
See other types in the comments 👇
Attention mechanisms allow models to dynamically focus on specific parts of their input when performing tasks. In our recent article, we discussed Multi-Head Latent Attention (MLA) in detail and now it's time to summarize other existing types of attention.
Here is a list of 15 types of attention mechanisms used in AI models:
1. Soft attention (Deterministic attention) -> Neural Machine Translation by Jointly Learning to Align and Translate (1409.0473)
Assigns a continuous weight distribution over all parts of the input. It produces a weighted sum of the input using attention weights that sum to 1.
2. Hard attention (Stochastic attention) -> Effective Approaches to Attention-based Neural Machine Translation (1508.04025)
Makes a discrete selection of some part of the input to focus on at each step, rather than attending to everything.
3. Self-attention -> Attention Is All You Need (1706.03762)
Each element in the sequence "looks" at other elements and "decides" how much to borrow from each of them for its new representation.
4. Cross-Attention (Encoder-Decoder attention) -> Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation (2104.08771)
The queries come from one sequence and the keys/values come from another sequence. It allows a model to combine information from two different sources.
5. Multi-Head Attention (MHA) -> Attention Is All You Need (1706.03762)
Multiple attention “heads” are run in parallel. The model computes several attention distributions (heads), each with its own set of learned projections of queries, keys, and values.
6. Multi-Head Latent Attention (MLA) -> DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model (2405.04434)
Extends MHA by incorporating a latent space where attention heads can dynamically learn different latent factors or representations.
7. Memory-Based attention -> End-To-End Memory Networks (1503.08895)
Involves an external memory and uses attention to read from and write to this memory.
See other types in the comments 👇
- 1 reply
liked 3 Spaces 12 months ago