content.raw: Diaries of Open Source. Part 4!
content.type: text
content.value: Diaries of Open Source. Part 4!
content.type: new_line
content.type: new_line
content.raw: 🌏Cohere and Cohere4AI release Command-R, a 35B model that is multilingual, RAG-optimized, and can manage tools!
content.type: text
content.value: 🌏Cohere and Cohere4AI release Command-R, a 35B model that is multilingual, RAG-optimized, and can manage tools!
content.type: new_line
content.raw: Model: 
content.type: text
content.value: Model: 
content.href: https://huggingface.co/CohereForAI/c4ai-command-r-v01
content.raw: https://huggingface.co/CohereForAI/c4ai-command-r-v01
content.type: link
content.type: new_line
content.raw: Blog post: 
content.type: text
content.value: Blog post: 
content.href: https://txt.cohere.com/command-r/
content.raw: https://txt.cohere.com/command-r/
content.type: link
content.type: new_line
content.type: new_line
content.raw: 🧑‍🍳StarChat2: A powerful code model that is conversational 
content.type: text
content.value: 🧑‍🍳StarChat2: A powerful code model that is conversational 
content.type: new_line
content.raw: Try it out: 
content.type: text
content.value: Try it out: 
content.raw: https://huggingface.co/spaces/HuggingFaceH4/starchat2-playground
content.id: HuggingFaceH4/starchat2-playground
content.type: space
content.type: resource
content.url: https://huggingface.co/spaces/HuggingFaceH4/starchat2-playground
content.type: new_line
content.raw: Repos: 
content.type: text
content.value: Repos: 
content.raw: https://huggingface.co/collections/HuggingFaceH4/starchat2-15b-65f068417b330fafad751fce
content.id: HuggingFaceH4/starchat2-15b-65f068417b330fafad751fce
content.type: collection
content.type: resource
content.url: https://huggingface.co/collections/HuggingFaceH4/starchat2-15b-65f068417b330fafad751fce
content.type: new_line
content.raw: Training code: 
content.type: text
content.value: Training code: 
content.href: https://github.com/huggingface/alignment-handbook/tree/main/recipes/starchat2-15b
content.raw: https://github.com/huggingface/alignment-handbook/tree/main/recipes/starchat2-15b
content.type: link
content.type: new_line
content.type: new_line
content.raw: 🐲Yi-9B: trained on 3 trillion tokens, this english-chinese LLM is quite good and with a very nice detailed report!
content.type: text
content.value: 🐲Yi-9B: trained on 3 trillion tokens, this english-chinese LLM is quite good and with a very nice detailed report!
content.type: new_line
content.raw: Model: 
content.type: text
content.value: Model: 
content.raw: https://huggingface.co/01-ai/Yi-9B
content.id: 01-ai/Yi-9B
content.type: model
content.type: resource
content.url: https://huggingface.co/01-ai/Yi-9B
content.type: new_line
content.raw: Paper: 
content.type: text
content.value: Paper: 
content.label: Yi: Open Foundation Models by 01.AI (2403.04652)
content.raw: https://huggingface.co/papers/2403.04652
content.id: 2403.04652
content.type: paper
content.type: resource
content.url: https://huggingface.co/papers/2403.04652
content.type: new_line
content.type: new_line
content.raw: 🐋DeepSeek-VL, 1.3B and 7B VLMs
content.type: text
content.value: 🐋DeepSeek-VL, 1.3B and 7B VLMs
content.type: new_line
content.raw: Paper: 
content.type: text
content.value: Paper: 
content.label: DeepSeek-VL: Towards Real-World Vision-Language Understanding (2403.05525)
content.raw: https://hf.co/papers/2403.05525
content.id: 2403.05525
content.type: paper
content.type: resource
content.url: https://hf.co/papers/2403.05525
content.type: new_line
content.raw: Large model: 
content.type: text
content.value: Large model: 
content.raw: https://huggingface.co/deepseek-ai/deepseek-vl-7b-chat
content.id: deepseek-ai/deepseek-vl-7b-chat
content.type: model
content.type: resource
content.url: https://huggingface.co/deepseek-ai/deepseek-vl-7b-chat
content.type: new_line
content.type: new_line
content.raw: ✍️Writer releases OmniACT: a dataset for multimodal agents for desktop and web.
content.type: text
content.value: ✍️Writer releases OmniACT: a dataset for multimodal agents for desktop and web.
content.type: new_line
content.raw: Dataset: 
content.type: text
content.value: Dataset: 
content.raw: https://huggingface.co/datasets/Writer/omniact
content.id: Writer/omniact
content.type: dataset
content.type: resource
content.url: https://huggingface.co/datasets/Writer/omniact
content.type: new_line
content.raw: Paper: 
content.type: text
content.value: Paper: 
content.label: OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist
    Autonomous Agents for Desktop and Web (2402.17553)
content.raw: https://huggingface.co/papers/2402.17553
content.id: 2402.17553
content.type: paper
content.type: resource
content.url: https://huggingface.co/papers/2402.17553
content.type: new_line
content.type: new_line
content.raw: 🍎Apple releases MobileCLIP: fast image-text models! 
content.type: text
content.value: 🍎Apple releases MobileCLIP: fast image-text models! 
content.href: https://github.com/apple/ml-mobileclip
content.raw: https://github.com/apple/ml-mobileclip
content.type: link
content.type: new_line
content.type: new_line
content.raw: 🦙💪LlamaGym - fine-tune LLM agents with RL in just a few lines of code! 
content.type: text
content.value: 🦙💪LlamaGym - fine-tune LLM agents with RL in just a few lines of code! 
content.href: https://github.com/KhoomeiK/LlamaGym
content.raw: https://github.com/KhoomeiK/LlamaGym
content.type: link
content.type: text
content.type: new_line
content.type: new_line
content.raw:  🖼️New multimodal leaderboard ConTextual 
content.type: text
content.value:  🖼️New multimodal leaderboard ConTextual 
content.href: https://huggingface.co/blog/leaderboard-contextual
content.raw: https://huggingface.co/blog/leaderboard-contextual
content.type: link
content.type: new_line
content.type: new_line
content.raw: 🎁 Design2Code: benchmark for multimodal LLMs for automating front-end development. 
content.type: text
content.value: 🎁 Design2Code: benchmark for multimodal LLMs for automating front-end development. 
content.type: new_line
content.raw: Dataset 
content.type: text
content.value: Dataset 
content.raw: https://huggingface.co/datasets/SALT-NLP/Design2Code
content.id: SALT-NLP/Design2Code
content.type: dataset
content.type: resource
content.url: https://huggingface.co/datasets/SALT-NLP/Design2Code
content.type: new_line
content.raw: Paper 
content.type: text
content.value: Paper 
content.label: Design2Code: How Far Are We From Automating Front-End Engineering? (2403.03163)
content.raw: https://huggingface.co/papers/2403.03163
content.id: 2403.03163
content.type: paper
content.type: resource
content.url: https://huggingface.co/papers/2403.03163
content.type: new_line
content.raw: Project 
content.type: text
content.value: Project 
content.href: https://salt-nlp.github.io/Design2Code/
content.raw: https://salt-nlp.github.io/Design2Code/
content.type: link
content.type: new_line
content.type: new_line
content.raw: You can find the previous part at 
content.type: text
content.value: You can find the previous part at 
content.href: https://huggingface.co/posts/osanseviero/633758457910104
content.raw: https://huggingface.co/posts/osanseviero/633758457910104
content.type: link
rawContent: Diaries of Open Source. Part 4!
  
  🌏Cohere and Cohere4AI release Command-R, a 35B model that is multilingual, RAG-optimized, and can manage tools!
  Model: https://huggingface.co/CohereForAI/c4ai-command-r-v01
  Blog post: https://txt.cohere.com/command-r/
  
  🧑‍🍳StarChat2: A powerful code model that is conversational 
  Try it out: https://huggingface.co/spaces/HuggingFaceH4/starchat2-playground
  Repos: https://huggingface.co/collections/HuggingFaceH4/starchat2-15b-65f068417b330fafad751fce
  Training code: https://github.com/huggingface/alignment-handbook/tree/main/recipes/starchat2-15b
  
  🐲Yi-9B: trained on 3 trillion tokens, this english-chinese LLM is quite good and with a very nice detailed report!
  Model: https://huggingface.co/01-ai/Yi-9B
  Paper: https://huggingface.co/papers/2403.04652
  
  🐋DeepSeek-VL, 1.3B and 7B VLMs
  Paper: https://hf.co/papers/2403.05525
  Large model: https://huggingface.co/deepseek-ai/deepseek-vl-7b-chat
  
  ✍️Writer releases OmniACT: a dataset for multimodal agents for desktop and web.
  Dataset: https://huggingface.co/datasets/Writer/omniact
  Paper: https://huggingface.co/papers/2402.17553
  
  🍎Apple releases MobileCLIP: fast image-text models! https://github.com/apple/ml-mobileclip
  
  🦙💪LlamaGym - fine-tune LLM agents with RL in just a few lines of code! https://github.com/KhoomeiK/LlamaGym 
  
   🖼️New multimodal leaderboard ConTextual https://huggingface.co/blog/leaderboard-contextual
  
  🎁 Design2Code: benchmark for multimodal LLMs for automating front-end development. 
  Dataset https://huggingface.co/datasets/SALT-NLP/Design2Code
  Paper https://huggingface.co/papers/2403.03163
  Project https://salt-nlp.github.io/Design2Code/
  
  You can find the previous part at https://huggingface.co/posts/osanseviero/633758457910104
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