content.raw: Dear all,
content.type: text
content.value: Dear all,
content.type: new_line
content.type: new_line
content.raw: We are happy to share that we have just open-sourced over 200 low-bit LLMs. For the MLX community, we have prepared 2-4 bit versions of mainstream LLMs. You can visit the following collection to access them: 
content.type: text
content.value: We are happy to share that we have just open-sourced over 200 low-bit LLMs. For the MLX community, we have prepared 2-4 bit versions of mainstream LLMs. You can visit the following collection to access them: 
content.raw: https://huggingface.co/collections/GreenBitAI/greenbitai-mlx-llm-6614eb6ceb8da657c2b4ed58
content.id: GreenBitAI/greenbitai-mlx-llm-6614eb6ceb8da657c2b4ed58
content.type: collection
content.type: resource
content.url: https://huggingface.co/collections/GreenBitAI/greenbitai-mlx-llm-6614eb6ceb8da657c2b4ed58
content.raw: . 
content.type: text
content.value: . 
content.type: new_line
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content.raw: These low-bit models can be conveniently used through our open-source tool at 
content.type: text
content.value: These low-bit models can be conveniently used through our open-source tool at 
content.href: https://github.com/GreenBitAI/gbx-lm
content.raw: https://github.com/GreenBitAI/gbx-lm
content.type: link
content.raw: . 
content.type: text
content.value: . 
content.type: new_line
content.type: new_line
content.raw: Compared to other open-source quantization algorithms, these models provide better accuracy retention. We have provided some model evaluation results here: 
content.type: text
content.value: Compared to other open-source quantization algorithms, these models provide better accuracy retention. We have provided some model evaluation results here: 
content.type: new_line
content.href: https://github.com/GreenBitAI/green-bit-llm/blob/main/green_bit_llm/evaluation/README.md
content.raw: https://github.com/GreenBitAI/green-bit-llm/blob/main/green_bit_llm/evaluation/README.md
content.type: link
content.raw: . 
content.type: text
content.value: . 
content.type: new_line
content.type: new_line
content.raw: You can also evaluate the models yourself using the evaluation script we provided.
content.type: text
content.value: You can also evaluate the models yourself using the evaluation script we provided.
rawContent: Dear all,
  
  We are happy to share that we have just open-sourced over 200 low-bit LLMs. For the MLX community, we have prepared 2-4 bit versions of mainstream LLMs. You can visit the following collection to access them: https://huggingface.co/collections/GreenBitAI/greenbitai-mlx-llm-6614eb6ceb8da657c2b4ed58. 
  
  These low-bit models can be conveniently used through our open-source tool at https://github.com/GreenBitAI/gbx-lm. 
  
  Compared to other open-source quantization algorithms, these models provide better accuracy retention. We have provided some model evaluation results here: 
  https://github.com/GreenBitAI/green-bit-llm/blob/main/green_bit_llm/evaluation/README.md. 
  
  You can also evaluate the models yourself using the evaluation script we provided.
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reactions.reaction: 👍
reactions.users: NicoNico adhisetiawan clem
commentators._id: 64b9f4569d8360edd98a9b62
commentators.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64b9f4569d8360edd98a9b62/HsPmCGBbgcrtj0VLVY_ex.png
commentators.fullname: fhsp
commentators.name: fhsp93
commentators.type: user