Command A: An Enterprise-Ready Large Language Model
Paper
•
2504.00698
•
Published
•
26
huggingPartyParis
community
AI & ML interests
None defined yet.
Recent Activity
View all activity
HuggingPartyParis's activity
afmck
authored
a
paper
2 months ago
MarcBotet
authored
a
paper
2 months ago
osanseviero
authored
a
paper
2 months ago
Xssama
authored
a
paper
8 months ago
devendrachaplot
authored
a
paper
8 months ago
Xssama
authored
a
paper
8 months ago
ragavsachdeva
authored
a
paper
9 months ago
ananthu-aniraj
authored
a
paper
9 months ago
HugoLaurencon
authored
a
paper
9 months ago
Sharvareev
authored
a
paper
10 months ago
ragavsachdeva
authored
a
paper
10 months ago
HugoLaurencon
authored
a
paper
about 1 year ago
HugoLaurencon
posted
an
update
about 1 year ago
Post
3055
We release Idefics2-chatty, the chatbot-optimized version of Idefics2:
HuggingFaceM4/idefics2-8b-chatty
Idefics2-chatty is better at following instructions and following Chain-of-Thoughts reasoning.
Moreover, we also release a paper, containing a lot of findings on how to build an efficient and performant Vision-Language Model: What matters when building vision-language models? (2405.02246)
How are you going to use the model, or what data are you going to fine-tune it on?
Idefics2-chatty is better at following instructions and following Chain-of-Thoughts reasoning.
Moreover, we also release a paper, containing a lot of findings on how to build an efficient and performant Vision-Language Model: What matters when building vision-language models? (2405.02246)
How are you going to use the model, or what data are you going to fine-tune it on?
-
- 5 replies
faaabian
authored
a
paper
about 1 year ago
HugoLaurencon
posted
an
update
about 1 year ago
Post
2562
Idefics2 is trained mostly on OBELICS, our open interleaved image-text document dataset.
Training on interleaved data is crucial to reaching high performance on VQA tasks, taking an arbitrary number of images as input, and doing in-context learning.
Dataset: HuggingFaceM4/OBELICS
Nomic visualization: https://atlas.nomic.ai/map/f2fba2aa-3647-4f49-a0f3-9347daeee499/ee4a84bd-f125-4bcc-a683-1b4e231cb10f
Link to OBELICS thread: https://twitter.com/HugoLaurencon/status/1694005892839006301
Training on interleaved data is crucial to reaching high performance on VQA tasks, taking an arbitrary number of images as input, and doing in-context learning.
Dataset: HuggingFaceM4/OBELICS
Nomic visualization: https://atlas.nomic.ai/map/f2fba2aa-3647-4f49-a0f3-9347daeee499/ee4a84bd-f125-4bcc-a683-1b4e231cb10f
Link to OBELICS thread: https://twitter.com/HugoLaurencon/status/1694005892839006301
HugoLaurencon
posted
an
update
about 1 year ago
Post
3007
The Cauldron is a massive collection of 50 high-quality datasets, all converted to the user/assistant format, and ready to use to fine-tune any Vision Language Model.
The Cauldron covers a wide range of tasks, including general visual question answering, counting, captioning, text transcription, document understanding, chart/figure understanding, table understanding, visual reasoning, geometry, spotting differences between 2 images or converting a screenshot to a code.
HuggingFaceM4/the_cauldron
The Cauldron covers a wide range of tasks, including general visual question answering, counting, captioning, text transcription, document understanding, chart/figure understanding, table understanding, visual reasoning, geometry, spotting differences between 2 images or converting a screenshot to a code.
HuggingFaceM4/the_cauldron
helboukkouri
authored
a
paper
about 1 year ago
osanseviero
posted
an
update
about 1 year ago
Post
13769
Diaries of Open Source. Part 15 🤗
🕵️♀️Idefics 2 is out, a multimodal open-source model with very nice capabilities
Models, demo, and datasets: HuggingFaceM4/idefics2-661d1971b7c50831dd3ce0fe
Blog: https://hf.co/blog/idefics2
💾Snowflake released snowflake-arctic-embed, a family of powerful small embedding models
Model: Snowflake/snowflake-arctic-embed-m
Blog: https://www.snowflake.com/blog/introducing-snowflake-arctic-embed-snowflakes-state-of-the-art-text-embedding-family-of-models/
✨Pile-T5, EleutherAI's T5 model trained on 2T tokens
Blog: https://blog.eleuther.ai/pile-t5/
Models: EleutherAI/pile-t5-65a76a0d0022dd270b385a66
GitHub: https://github.com/EleutherAI/improved-t5
🤖CodeQwen1.5-7B base and chat models. Models trained on 3T tokens strong benchmark results for code generation, editing and SQL
Blog post: https://qwenlm.github.io/blog/codeqwen1.5/
Demo: Qwen/CodeQwen1.5-7b-Chat-demo
Models: Qwen/CodeQwen1.5-7B and Qwen/CodeQwen1.5-7B-Chat
Misc
🦉 DocOwl1.5: Unified Stucture Learning for OCR-free Document Understanding mPLUG/DocOwl
👀Cerule - a tiny Vision LM model Tensoic/Cerule-v0.1
ChemLLM - a LLM for chemistry and molecule science ⚗️https://hf.co/AI4Chem/ChemLLM-7B-Chat-1.5-DPO
Distil Whisper Large
📝New pdf/OCR datasets with 19 samples pixparse/pdf-document-ocr-datasets-660701430b0346f97c4bc628
🔥Gretel AI high quality text-to-sql synthetic dataset gretelai/synthetic_text_to_sql
🕵️♀️Idefics 2 is out, a multimodal open-source model with very nice capabilities
Models, demo, and datasets: HuggingFaceM4/idefics2-661d1971b7c50831dd3ce0fe
Blog: https://hf.co/blog/idefics2
💾Snowflake released snowflake-arctic-embed, a family of powerful small embedding models
Model: Snowflake/snowflake-arctic-embed-m
Blog: https://www.snowflake.com/blog/introducing-snowflake-arctic-embed-snowflakes-state-of-the-art-text-embedding-family-of-models/
✨Pile-T5, EleutherAI's T5 model trained on 2T tokens
Blog: https://blog.eleuther.ai/pile-t5/
Models: EleutherAI/pile-t5-65a76a0d0022dd270b385a66
GitHub: https://github.com/EleutherAI/improved-t5
🤖CodeQwen1.5-7B base and chat models. Models trained on 3T tokens strong benchmark results for code generation, editing and SQL
Blog post: https://qwenlm.github.io/blog/codeqwen1.5/
Demo: Qwen/CodeQwen1.5-7b-Chat-demo
Models: Qwen/CodeQwen1.5-7B and Qwen/CodeQwen1.5-7B-Chat
Misc
🦉 DocOwl1.5: Unified Stucture Learning for OCR-free Document Understanding mPLUG/DocOwl
👀Cerule - a tiny Vision LM model Tensoic/Cerule-v0.1
ChemLLM - a LLM for chemistry and molecule science ⚗️https://hf.co/AI4Chem/ChemLLM-7B-Chat-1.5-DPO
Distil Whisper Large
📝New pdf/OCR datasets with 19 samples pixparse/pdf-document-ocr-datasets-660701430b0346f97c4bc628
🔥Gretel AI high quality text-to-sql synthetic dataset gretelai/synthetic_text_to_sql
-
- 5 replies
HugoLaurencon
posted
an
update
about 1 year ago
Post
3111
We release Idefics2-8B, a foundation vision language model with SOTA results for its size on many benchmarks.
For Idefics2, we adopted a simple architecture:
-Images are fed to a vision encoder, then to a modality projection to match the input dimension of the LLM, and finally to a perceiver resampler for efficient pooling.
-Interleaved image-text data are then passed to the LLM.
During the pre-training:
-The modality projection and perceiver resampler weights are newly initialized.
-We start with pre-trained models for the vision encoder and the LLM, and continue the training with LoRA.
-In total, we see 1.5T images!
We pre-train on 3 types of data, all publicly available:
-Interleaved image-text documents: our dataset OBELICS HuggingFaceM4/OBELICS
-Image caption pairs: only synthetic captions!
-PDF documents: IDL and PDFA
We kept the aspect ratio of the images with the Patch n' Pack strategy, with a resolution of up to 980x980.
At inference, it's also more efficient for lower-resolution images.
For the SFT, we build The Cauldron, a collection of 50 high-quality datasets in the user/assistant format.
It is a ready-to-use dataset for the fine-tuning of any VLM.
HuggingFaceM4/the_cauldron
Most current models, like LLaVA-NeXT, encode images with an excessive number of tokens, like 2880.
Instead, we put a focus on being efficient at inference by training on a mix of images encoded with 64 tokens, and 320 tokens.
The result is that we perform favorably compared to the best models in our size class, while being efficient at inference.
For Idefics2, we adopted a simple architecture:
-Images are fed to a vision encoder, then to a modality projection to match the input dimension of the LLM, and finally to a perceiver resampler for efficient pooling.
-Interleaved image-text data are then passed to the LLM.
During the pre-training:
-The modality projection and perceiver resampler weights are newly initialized.
-We start with pre-trained models for the vision encoder and the LLM, and continue the training with LoRA.
-In total, we see 1.5T images!
We pre-train on 3 types of data, all publicly available:
-Interleaved image-text documents: our dataset OBELICS HuggingFaceM4/OBELICS
-Image caption pairs: only synthetic captions!
-PDF documents: IDL and PDFA
We kept the aspect ratio of the images with the Patch n' Pack strategy, with a resolution of up to 980x980.
At inference, it's also more efficient for lower-resolution images.
For the SFT, we build The Cauldron, a collection of 50 high-quality datasets in the user/assistant format.
It is a ready-to-use dataset for the fine-tuning of any VLM.
HuggingFaceM4/the_cauldron
Most current models, like LLaVA-NeXT, encode images with an excessive number of tokens, like 2880.
Instead, we put a focus on being efficient at inference by training on a mix of images encoded with 64 tokens, and 320 tokens.
The result is that we perform favorably compared to the best models in our size class, while being efficient at inference.
osanseviero
posted
an
update
about 1 year ago
Post
11303
Diaries of Open Source. Part 14 🤗
🔥CohereForAI releases Command R+, an open 104B model with:
- Tool usage capabilities
- Specialized in RAGs
- Multilingual
It's one of the first models to surpass GPT-4 in the lmsys arena, check it out!
Model: https://hf.co/CohereForAI/c4ai-command-r-plus
Official demo: https://hf.co/spaces/CohereForAI/c4ai-command-r-plus
Quantized: https://hf.co/CohereForAI/c4ai-command-r-plus-4bit
🎉Google releases a new version of their Gemma instruct models, with improved quality, nicer to converse, and a fancier RL algorithm. The model is similar to Llama 2 70B in the Chat Arena!
Models: google/gemma-release-65d5efbccdbb8c4202ec078b
Try it out in HuggingChat https://hf.co/chat/models/google/gemma-1.1-7b-it
🪄VoiceCraft, a speech editing and TTS SOTA open model
Paper: VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild (2403.16973)
Model: pyp1/VoiceCraft
💻Google released CodeGemma, a family of code generation, completion, and chat models
Blog post: https://hf.co/blog/codegemma
Models: google/codegemma-release-66152ac7b683e2667abdee11
Report: https://storage.googleapis.com/deepmind-media/gemma/codegemma_report.pdf
Misc models:
🦖T-Rex2, a very powerful object detection model for many applications https://github.com/IDEA-Research/T-Rex
👀 CT-RATE : A 3D dataset paired with text reports ibrahimhamamci/CT-RATE
🐙Octopus v2: a Gemma-based model trained for Android API - extremely fast, better than Llama+RAG, great results NexaAIDev/Octopus-v2
🔥CohereForAI releases Command R+, an open 104B model with:
- Tool usage capabilities
- Specialized in RAGs
- Multilingual
It's one of the first models to surpass GPT-4 in the lmsys arena, check it out!
Model: https://hf.co/CohereForAI/c4ai-command-r-plus
Official demo: https://hf.co/spaces/CohereForAI/c4ai-command-r-plus
Quantized: https://hf.co/CohereForAI/c4ai-command-r-plus-4bit
🎉Google releases a new version of their Gemma instruct models, with improved quality, nicer to converse, and a fancier RL algorithm. The model is similar to Llama 2 70B in the Chat Arena!
Models: google/gemma-release-65d5efbccdbb8c4202ec078b
Try it out in HuggingChat https://hf.co/chat/models/google/gemma-1.1-7b-it
🪄VoiceCraft, a speech editing and TTS SOTA open model
Paper: VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild (2403.16973)
Model: pyp1/VoiceCraft
💻Google released CodeGemma, a family of code generation, completion, and chat models
Blog post: https://hf.co/blog/codegemma
Models: google/codegemma-release-66152ac7b683e2667abdee11
Report: https://storage.googleapis.com/deepmind-media/gemma/codegemma_report.pdf
Misc models:
🦖T-Rex2, a very powerful object detection model for many applications https://github.com/IDEA-Research/T-Rex
👀 CT-RATE : A 3D dataset paired with text reports ibrahimhamamci/CT-RATE
🐙Octopus v2: a Gemma-based model trained for Android API - extremely fast, better than Llama+RAG, great results NexaAIDev/Octopus-v2
-
- 2 replies