teapotai/teapotllm
Text2Text Generation
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Carlo Moro
cnmoro
·
AI & ML interests
I like small & fast models
Trabalhando nos menores modelos em português que existem
https://ko-fi.com/cnmoro
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liked
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about 10 hours ago
teapotai/teapotllm
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a dataset
1 day ago
cnmoro/reasoning-v1-20m-portuguese
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cnmoro's activity
liked
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model
about 10 hours ago
reacted to
tomaarsen's
post with 🔥
1 day ago
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1826
‼️Sentence Transformers v4.0 is out! You can now train and finetune reranker models with multi-GPU training, bf16 support, loss logging, callbacks & much more. I also prove that finetuning on your domain helps much more than you might think.
1️⃣ Reranker Training Refactor
Reranker models can now be trained using an extensive trainer with a lot of powerful features:
- MultiGPU Training (Data Parallelism (DP) and Distributed Data Parallelism (DDP))
- bf16 training support; loss logging
- Evaluation datasets + evaluation loss
- Improved callback support + an excellent Weights & Biases integration
- Gradient checkpointing, gradient accumulation
- Model card generation
- Resuming from a training checkpoint without performance loss
- Hyperparameter Optimization
and much more!
Read my detailed blogpost to learn about the components that make up this new training approach: https://huggingface.co/blog/train-reranker
Notably, the release is fully backwards compatible: all deprecations are soft, meaning that they still work but emit a warning informing you how to upgrade.
2️⃣ New Reranker Losses
- 11 new losses:
- 2 traditional losses: BinaryCrossEntropy and CrossEntropy
- 2 distillation losses: MSE and MarginMSE
- 2 in-batch negatives losses: MNRL (a.k.a. InfoNCE) and CMNRL
- 5 learning to rank losses: Lambda, p-ListMLE, ListNet, RankNet, ListMLE
3️⃣ New Reranker Documentation
- New Training Overview, Loss Overview, API Reference docs
- 5 new, 1 refactored training examples docs pages
- 13 new, 6 refactored training scripts
- Migration guides (2.x -> 3.x, 3.x -> 4.x)
4️⃣ Blogpost
Alongside the release, I've written a blogpost where I finetune ModernBERT on a generic question-answer dataset. My finetunes easily outperform all general-purpose reranker models, even models 4x as big. Finetuning on your domain is definitely worth it: https://huggingface.co/blog/train-reranker
See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/v4.0.1
1️⃣ Reranker Training Refactor
Reranker models can now be trained using an extensive trainer with a lot of powerful features:
- MultiGPU Training (Data Parallelism (DP) and Distributed Data Parallelism (DDP))
- bf16 training support; loss logging
- Evaluation datasets + evaluation loss
- Improved callback support + an excellent Weights & Biases integration
- Gradient checkpointing, gradient accumulation
- Model card generation
- Resuming from a training checkpoint without performance loss
- Hyperparameter Optimization
and much more!
Read my detailed blogpost to learn about the components that make up this new training approach: https://huggingface.co/blog/train-reranker
Notably, the release is fully backwards compatible: all deprecations are soft, meaning that they still work but emit a warning informing you how to upgrade.
2️⃣ New Reranker Losses
- 11 new losses:
- 2 traditional losses: BinaryCrossEntropy and CrossEntropy
- 2 distillation losses: MSE and MarginMSE
- 2 in-batch negatives losses: MNRL (a.k.a. InfoNCE) and CMNRL
- 5 learning to rank losses: Lambda, p-ListMLE, ListNet, RankNet, ListMLE
3️⃣ New Reranker Documentation
- New Training Overview, Loss Overview, API Reference docs
- 5 new, 1 refactored training examples docs pages
- 13 new, 6 refactored training scripts
- Migration guides (2.x -> 3.x, 3.x -> 4.x)
4️⃣ Blogpost
Alongside the release, I've written a blogpost where I finetune ModernBERT on a generic question-answer dataset. My finetunes easily outperform all general-purpose reranker models, even models 4x as big. Finetuning on your domain is definitely worth it: https://huggingface.co/blog/train-reranker
See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/v4.0.1
updated
a
dataset
1 day ago
liked
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1 day ago
liked
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2 days ago
liked
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dataset
2 days ago
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hanzla's
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4 days ago
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5778
👋 Hi all!
For any AI agent, internet search 🔎 is an important tool. However, with APIs like Tavily and Exa, it becomes really difficult to keep up with the cost. In some cases, these Internet APIs cost more than the LLM.
To solve, this, I am making a playwright wrapper API on top of publicly available searXNG instances. This will enable agent applications to fetch internet results for free.
Currently, I have set up a basic GitHub repo, and I will continue developing advanced search features, such as image search 🖼️
Github: https://github.com/HanzlaJavaid/Free-Search/tree/main
🚀 Try the deployed version: https://freesearch.replit.app/docs
If you find this useful, consider starring ⭐️ the GitHub repository to support further development!
For any AI agent, internet search 🔎 is an important tool. However, with APIs like Tavily and Exa, it becomes really difficult to keep up with the cost. In some cases, these Internet APIs cost more than the LLM.
To solve, this, I am making a playwright wrapper API on top of publicly available searXNG instances. This will enable agent applications to fetch internet results for free.
Currently, I have set up a basic GitHub repo, and I will continue developing advanced search features, such as image search 🖼️
Github: https://github.com/HanzlaJavaid/Free-Search/tree/main
🚀 Try the deployed version: https://freesearch.replit.app/docs
If you find this useful, consider starring ⭐️ the GitHub repository to support further development!
liked
a
model
6 days ago
published
a
dataset
6 days ago
liked
a
model
7 days ago
liked
a
model
8 days ago
liked
2
models
9 days ago
liked
a
dataset
10 days ago
updated
a
model
11 days ago
published
a
model
11 days ago
liked
a
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11 days ago
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mlabonne's
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11 days ago
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6003
✂️ 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