🤗 Serve any model with Inference Endpoints + Custom Handlers
•
3
Alvaro Bartolome
alvarobartt
AI & ML interests
☁️ cloud machine learning @huggingface and open source passionate
Recent Activity
upvoted
a
paper
about 24 hours ago
Towards Best Practices for Open Datasets for LLM Training
new activity
1 day ago
Alibaba-NLP/gte-multilingual-base:HERE IS HOW YOU USE THIS WITH TEI OR INFERENCE ENDPOINTS
new activity
1 day ago
Alibaba-NLP/gte-multilingual-base:Update `config.json` to support TEI
Articles
Organizations
alvarobartt's activity
replied to
burtenshaw's
post
2 days ago
oh nice to see you here @bechavarren 👋🏻
reacted to
mlabonne's
post with 🤗🔥
2 days ago
Post
2032
🆕 LLM Course 2025 edition!
I updated the LLM Scientist roadmap and added a ton of new information and references. It covers training, datasets, evaluation, quantization, and new trends like test-time compute scaling.
The LLM Course has been incredibly popular (41.3k stars!) and I've been touched to receive many, many messages about how it helped people in their careers.
I know how difficult this stuff can be, so I'm super proud of the impact it had. I want to keep updating it in 2025, especially with the LLM Engineer roadmap.
Thanks everyone, hope you'll enjoy it!
💻 LLM Course: https://huggingface.co/blog/mlabonne/llm-course
I updated the LLM Scientist roadmap and added a ton of new information and references. It covers training, datasets, evaluation, quantization, and new trends like test-time compute scaling.
The LLM Course has been incredibly popular (41.3k stars!) and I've been touched to receive many, many messages about how it helped people in their careers.
I know how difficult this stuff can be, so I'm super proud of the impact it had. I want to keep updating it in 2025, especially with the LLM Engineer roadmap.
Thanks everyone, hope you'll enjoy it!
💻 LLM Course: https://huggingface.co/blog/mlabonne/llm-course
reacted to
AdinaY's
post with 🚀🔥
about 2 months ago
Post
1611
🌊 The wave of reasoning models from the Chinese community has arrived!
🚀 Marco-o1 by AIDC, Alibaba
👉 AIDC-AI/Marco-o1
✨ QwQ by Qwen, Alibaba
👉 Qwen/qwq-674762b79b75eac01735070a
🌟 Skywork-o1 by Kunlun Tech
👉 Skywork/skywork-o1-open-67453df58e12f6c3934738d0
🔥 Xkev/Llama-3.2V-11B-cot by PKU Yuan group
👉 Xkev/Llama-3.2V-11B-cot
💡 DeepSeek-R1-Lite-Preview by DeepSeek AI
👉 https://chat.deepseek.com/
🔍 InternThinker Preview by Shanghai AI Lab
👉 https://sso.openxlab.org.cn/login?redirect=https://internlm-chat.intern-ai.org.cn/&clientId=ebmrvod6yo0nlzaek1yp
📘 k0-math by Moonshot AI
🚀 https://kimi.moonshot.cn/ ( coming soon! )
Who's next? 👀
zh-ai-community/reasoning-models-67409fb3aa1ed78f10087cd7
🚀 Marco-o1 by AIDC, Alibaba
👉 AIDC-AI/Marco-o1
✨ QwQ by Qwen, Alibaba
👉 Qwen/qwq-674762b79b75eac01735070a
🌟 Skywork-o1 by Kunlun Tech
👉 Skywork/skywork-o1-open-67453df58e12f6c3934738d0
🔥 Xkev/Llama-3.2V-11B-cot by PKU Yuan group
👉 Xkev/Llama-3.2V-11B-cot
💡 DeepSeek-R1-Lite-Preview by DeepSeek AI
👉 https://chat.deepseek.com/
🔍 InternThinker Preview by Shanghai AI Lab
👉 https://sso.openxlab.org.cn/login?redirect=https://internlm-chat.intern-ai.org.cn/&clientId=ebmrvod6yo0nlzaek1yp
📘 k0-math by Moonshot AI
🚀 https://kimi.moonshot.cn/ ( coming soon! )
Who's next? 👀
zh-ai-community/reasoning-models-67409fb3aa1ed78f10087cd7
reacted to
andito's
post with 🔥👀
about 2 months ago
Post
1844
SmolVLM speeding locally on a laptop thanks to mlx-vlm and
@Gradio ! Try it with two lines:
pip install git+https://github.com/andimarafioti/mlx-vlm.git@stream-generate-fix
python -m mlx_vlm.chat_ui --model mlx-community/SmolVLM-Instruct-8bit
Gotta love the MLX community! Big thanks to @pcuenq and @prince_canuma !
@Gradio ! Try it with two lines:
pip install git+https://github.com/andimarafioti/mlx-vlm.git@stream-generate-fix
python -m mlx_vlm.chat_ui --model mlx-community/SmolVLM-Instruct-8bit
Gotta love the MLX community! Big thanks to @pcuenq and @prince_canuma !
reacted to
danielhanchen's
post with 🔥
about 2 months ago
Post
1405
Vision finetuning is in 🦥Unsloth! You can now finetune Llama 3.2, Qwen2 VL, Pixtral and all Llava variants up to 2x faster and with up to 70% less VRAM usage! Colab to finetune Llama 3.2: https://colab.research.google.com/drive/1j0N4XTY1zXXy7mPAhOC1_gMYZ2F2EBlk?usp=sharing
- 1 reply
reacted to
clem's
post with 🔥
3 months ago
Post
4446
This is no Woodstock AI but will be fun nonetheless haha. I’ll be hosting a live workshop with team members next week about the Enterprise Hugging Face hub.
1,000 spots available first-come first serve with some surprises during the stream!
You can register and add to your calendar here: https://streamyard.com/watch/JS2jHsUP3NDM
1,000 spots available first-come first serve with some surprises during the stream!
You can register and add to your calendar here: https://streamyard.com/watch/JS2jHsUP3NDM
- 4 replies
posted
an
update
5 months ago
Post
2914
🤗 Serving Meta Llama 3.1 405B on Google Cloud is now possible via the Hugging Face Deep Learning Containers (DLCs) for Text Generation Inference (TGI)
In this post, we showcase how to deploy https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct-FP8 on an A3 instance with 8 x H100 GPUs on Vertex AI
Thanks to the Hugging Face DLCs for TGI and Google Cloud Vertex AI, deploying a high-performance text generation container for serving Large Language Models (LLMs) has never been easier. And we’re not going to stop here – stay tuned as we enable more experiences to build AI with open models on Google Cloud!
Read the full post at https://huggingface.co/blog/llama31-on-vertex-ai
In this post, we showcase how to deploy https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct-FP8 on an A3 instance with 8 x H100 GPUs on Vertex AI
Thanks to the Hugging Face DLCs for TGI and Google Cloud Vertex AI, deploying a high-performance text generation container for serving Large Language Models (LLMs) has never been easier. And we’re not going to stop here – stay tuned as we enable more experiences to build AI with open models on Google Cloud!
Read the full post at https://huggingface.co/blog/llama31-on-vertex-ai
reacted to
merve's
post with 🔥
5 months ago
Post
4977
Idefics3-Llama is out! 💥💥
Model: HuggingFaceM4/Idefics3-8B-Llama3
Demo: HuggingFaceM4/idefics3
It's a multimodal model based on Llama 3.1 that accepts an arbitrary number of interleaved images with text with a huge context window (10k tokens!) ✨
Supported by Hugging Face transformers 🤗
Model: HuggingFaceM4/Idefics3-8B-Llama3
Demo: HuggingFaceM4/idefics3
It's a multimodal model based on Llama 3.1 that accepts an arbitrary number of interleaved images with text with a huge context window (10k tokens!) ✨
Supported by Hugging Face transformers 🤗
- 2 replies
reacted to
dvilasuero's
post with 🤗🚀🔥
7 months ago
Post
8112
Today is a huge day in Argilla’s history. We couldn’t be more excited to share this with the community: we’re joining Hugging Face!
We’re embracing a larger mission, becoming part of a brilliant and kind team and a shared vision about the future of AI.
Over the past year, we’ve been collaborating with Hugging Face on countless projects: launching partner of Docker Spaces, empowering the community to clean Alpaca translations into Spanish and other languages, launching argilla/notus-7b-v1 building on Zephyr’s learnings, the Data is Better Together initiative with hundreds of community contributors, or releasing argilla/OpenHermesPreferences, one of the largest open preference tuning datasets
After more than 2,000 Slack messages and over 60 people collaborating for over a year, it already felt like we were part of the same team, pushing in the same direction. After a week of the smoothest transition you can imagine, we’re now the same team.
To those of you who’ve been following us, this won’t be a huge surprise, but it will be a big deal in the coming months. This acquisition means we’ll double down on empowering the community to build and collaborate on high quality datasets, we’ll bring full support for multimodal datasets, and we’ll be in a better place to collaborate with the Open Source AI community. For enterprises, this means that the Enterprise Hub will unlock highly requested features like single sign-on and integration with Inference Endpoints.
As a founder, I am proud of the Argilla team. We're now part of something bigger and a larger team but with the same values, culture, and goals. Grateful to have shared this journey with my beloved co-founders Paco and Amélie.
Finally, huge thanks to the Chief Llama Officer @osanseviero for sparking this and being such a great partner during the acquisition process.
Would love to answer any questions you have so feel free to add them below!
We’re embracing a larger mission, becoming part of a brilliant and kind team and a shared vision about the future of AI.
Over the past year, we’ve been collaborating with Hugging Face on countless projects: launching partner of Docker Spaces, empowering the community to clean Alpaca translations into Spanish and other languages, launching argilla/notus-7b-v1 building on Zephyr’s learnings, the Data is Better Together initiative with hundreds of community contributors, or releasing argilla/OpenHermesPreferences, one of the largest open preference tuning datasets
After more than 2,000 Slack messages and over 60 people collaborating for over a year, it already felt like we were part of the same team, pushing in the same direction. After a week of the smoothest transition you can imagine, we’re now the same team.
To those of you who’ve been following us, this won’t be a huge surprise, but it will be a big deal in the coming months. This acquisition means we’ll double down on empowering the community to build and collaborate on high quality datasets, we’ll bring full support for multimodal datasets, and we’ll be in a better place to collaborate with the Open Source AI community. For enterprises, this means that the Enterprise Hub will unlock highly requested features like single sign-on and integration with Inference Endpoints.
As a founder, I am proud of the Argilla team. We're now part of something bigger and a larger team but with the same values, culture, and goals. Grateful to have shared this journey with my beloved co-founders Paco and Amélie.
Finally, huge thanks to the Chief Llama Officer @osanseviero for sparking this and being such a great partner during the acquisition process.
Would love to answer any questions you have so feel free to add them below!
- 28 replies
reacted to
tomaarsen's
post with 🔥❤️
8 months ago
Post
2001
‼️Sentence Transformers v3.0 is out! You can now train and finetune embedding models with multi-GPU training, bf16 support, loss logging, callbacks & much more. I also release 50+ datasets to train on.
1️⃣ Training Refactor
Embedding 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
- Improved 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-sentence-transformers
2️⃣ Similarity Score
Not sure how to compare embeddings? Don't worry, you can now use
3️⃣ Additional Kwargs
Sentence Transformers relies on various Transformers instances (AutoModel, AutoTokenizer, AutoConfig), but it was hard to provide valuable keyword arguments to these (like 'torch_dtype=torch.bfloat16' to load a model a lower precision for 2x inference speedup). This is now easy!
4️⃣ Hyperparameter Optimization
Sentence Transformers now ships with HPO, allowing you to effectively choose your hyperparameters for your data and task.
5️⃣ Dataset Release
To help you out with finetuning models, I've released 50+ ready-to-go datasets that can be used with training or finetuning embedding models: sentence-transformers/embedding-model-datasets-6644d7a3673a511914aa7552
Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.0.0
1️⃣ Training Refactor
Embedding 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
- Improved 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-sentence-transformers
2️⃣ Similarity Score
Not sure how to compare embeddings? Don't worry, you can now use
model.similarity(embeddings1, embeddings2) and you'll get your similarity scores immediately. Model authors can specify their desired similarity score, so you don't have to worry about it anymore!3️⃣ Additional Kwargs
Sentence Transformers relies on various Transformers instances (AutoModel, AutoTokenizer, AutoConfig), but it was hard to provide valuable keyword arguments to these (like 'torch_dtype=torch.bfloat16' to load a model a lower precision for 2x inference speedup). This is now easy!
4️⃣ Hyperparameter Optimization
Sentence Transformers now ships with HPO, allowing you to effectively choose your hyperparameters for your data and task.
5️⃣ Dataset Release
To help you out with finetuning models, I've released 50+ ready-to-go datasets that can be used with training or finetuning embedding models: sentence-transformers/embedding-model-datasets-6644d7a3673a511914aa7552
Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.0.0
reacted to
merve's
post with 🚀
8 months ago
Post
1761
New open Vision Language Model by
@Google
: PaliGemma 💙🤍
📝 Comes in 3B, pretrained, mix and fine-tuned models in 224, 448 and 896 resolution
🧩 Combination of Gemma 2B LLM and SigLIP image encoder
🤗 Supported in transformers
PaliGemma can do..
🧩 Image segmentation and detection! 🤯
📑 Detailed document understanding and reasoning
🙋 Visual question answering, captioning and any other VLM task!
Read our blog 🔖 hf.co/blog/paligemma
Try the demo 🪀 hf.co/spaces/google/paligemma
Check out the Spaces and the models all in the collection 📚 google/paligemma-release-6643a9ffbf57de2ae0448dda
Collection of fine-tuned PaliGemma models google/paligemma-ft-models-6643b03efb769dad650d2dda
📝 Comes in 3B, pretrained, mix and fine-tuned models in 224, 448 and 896 resolution
🧩 Combination of Gemma 2B LLM and SigLIP image encoder
🤗 Supported in transformers
PaliGemma can do..
🧩 Image segmentation and detection! 🤯
📑 Detailed document understanding and reasoning
🙋 Visual question answering, captioning and any other VLM task!
Read our blog 🔖 hf.co/blog/paligemma
Try the demo 🪀 hf.co/spaces/google/paligemma
Check out the Spaces and the models all in the collection 📚 google/paligemma-release-6643a9ffbf57de2ae0448dda
Collection of fine-tuned PaliGemma models google/paligemma-ft-models-6643b03efb769dad650d2dda
- 13 replies
posted
an
update
8 months ago
Post
3169
🔥 Prometheus 2 was recently released by Kaist AI as an alternative and closely mirroring both human and GPT-4 evaluation, and surpassing the former Prometheus!
prometheus-eval/prometheus-7b-v2.0
prometheus-eval/prometheus-8x7b-v2.0
🌬️Fine-tuned on top of mistralai/Mistral-7B-Instruct-v0.2 and mistralai/Mixtral-8x7B-Instruct-v0.1
🗂️The datasets used for fine-tuning have been publicly released i.e. prometheus-eval/Feedback-Collection and prometheus-eval/Preference-Collection
🤝🏻Unified LM evaluator for absolute (a single prompt-completion pair) and relative (two completions for a given prompt) due to model merging
❌No longer needs a mandatory reference / golden answer, but can still be provided optionally
🔝Surpasses the former version of Prometheus, and has a high correlation with human, GPT-4, and Claude 3 Opus scores when evaluating LMs
📝Apache 2.0 license
Long-story short, an amazing job from Kaist AI bridging the gap with LLM evaluators other than proprietary and bigger models!
This week at Argilla, we decided to add a new task to use Prometheus 2 as an LLM evaluator using
😱 Using
Find the generated dataset and the code at distilabel-internal-testing/instruction-dataset-prometheus
prometheus-eval/prometheus-7b-v2.0
prometheus-eval/prometheus-8x7b-v2.0
🌬️Fine-tuned on top of mistralai/Mistral-7B-Instruct-v0.2 and mistralai/Mixtral-8x7B-Instruct-v0.1
🗂️The datasets used for fine-tuning have been publicly released i.e. prometheus-eval/Feedback-Collection and prometheus-eval/Preference-Collection
🤝🏻Unified LM evaluator for absolute (a single prompt-completion pair) and relative (two completions for a given prompt) due to model merging
❌No longer needs a mandatory reference / golden answer, but can still be provided optionally
🔝Surpasses the former version of Prometheus, and has a high correlation with human, GPT-4, and Claude 3 Opus scores when evaluating LMs
📝Apache 2.0 license
Long-story short, an amazing job from Kaist AI bridging the gap with LLM evaluators other than proprietary and bigger models!
This week at Argilla, we decided to add a new task to use Prometheus 2 as an LLM evaluator using
distilabel, so we implemented PrometheusEval.😱 Using
PrometheusEval running their 7B variant with vLLM in a single L40 on top of
HuggingFaceH4/instruction-dataset, we got the 327 existing prompt-completion pairs evaluated and pushed to the Hub in less than 2 minutes!Find the generated dataset and the code at distilabel-internal-testing/instruction-dataset-prometheus
- 1 reply
reacted to
davanstrien's
post with 🤗
9 months ago
Post
1673
As part of the Data is Better Together MPEP project, we are now at the point where some translation efforts have successfully translated 500 highly ranked prompts into a new target language (amazing work from
@Rijgersberg
et al!)
Our next step is to use these translated prompts to evaluate the performance of LLMs for non English languages.
Does LLM, as a judge, work outside of English?
Ideally, it would be compelling to leverage LLMs to judge models for non-English since this significantly lowers the barrier to evaluating models (although it doesn't remove this barrier altogether).
What we want to know is:
- does auto/LLM eval work in general for a particular language
- which model(s) works best as a judge
- do LLMs' judgments of non-English models match human preferences?
We're starting to think about how to approach this. If you have any ideas of possible approaches feel free to comment or join the discussion here: https://github.com/huggingface/data-is-better-together/issues/61
Other ideas...
Could an approach like Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models (2404.18796) with the SOA models for a particular language work? i.e., choose 4 of the best open LLMs for Arabic and use those at the pool of raters rather than relying on one powerful judge LLM?
Our next step is to use these translated prompts to evaluate the performance of LLMs for non English languages.
Does LLM, as a judge, work outside of English?
Ideally, it would be compelling to leverage LLMs to judge models for non-English since this significantly lowers the barrier to evaluating models (although it doesn't remove this barrier altogether).
What we want to know is:
- does auto/LLM eval work in general for a particular language
- which model(s) works best as a judge
- do LLMs' judgments of non-English models match human preferences?
We're starting to think about how to approach this. If you have any ideas of possible approaches feel free to comment or join the discussion here: https://github.com/huggingface/data-is-better-together/issues/61
Other ideas...
Could an approach like Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models (2404.18796) with the SOA models for a particular language work? i.e., choose 4 of the best open LLMs for Arabic and use those at the pool of raters rather than relying on one powerful judge LLM?
reacted to
davanstrien's
post with 🤗
9 months ago
Post
1543
Can you create domain-specific synthetic datasets in under 20 minutes?
@burtenshaw recently launched the Domain Specific Dataset Project as part of Data is Better Together. As part of this, Ben created a Space that you can use to define some key perspectives and concepts from a domain. This seed dataset can then be used to generate a synthetic dataset for a particular domain.
In less than 30 minutes this afternoon, I created a domain-specific dataset focused on data-centric machine learning using these tools: davanstrien/data-centric-ml-sft.
You can create your own domain specific datasets using this approach. Find the steps to follow here: https://github.com/huggingface/data-is-better-together/blob/main/domain-specific-datasets/README.md
@burtenshaw recently launched the Domain Specific Dataset Project as part of Data is Better Together. As part of this, Ben created a Space that you can use to define some key perspectives and concepts from a domain. This seed dataset can then be used to generate a synthetic dataset for a particular domain.
In less than 30 minutes this afternoon, I created a domain-specific dataset focused on data-centric machine learning using these tools: davanstrien/data-centric-ml-sft.
You can create your own domain specific datasets using this approach. Find the steps to follow here: https://github.com/huggingface/data-is-better-together/blob/main/domain-specific-datasets/README.md
- 1 reply