Introducing HuggingFace blog for Chinese speakers: Fostering Collaboration with the Chinese AI community
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Tiezhen WANG
xianbao
AI & ML interests
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posted
an
update
6 days ago
Post
1141
Why Apache 2.0 Matters for LLMs 🤔
@01AI_Yi recently switched from a permissive & commercially friendly license, to Apache 2.0. And the community loved it! 🚀
@JustinLin610 also had a poll on model license and the majority votes for Apache 2.0.
Why it is a Big Deal? ⬇️
📚 Legal Simplicity: Custom licenses need costly & time-consuming legal review. Apache 2.0 is well-known & easier for legal teams to handle.
👩💻 Developer-Friendly: Legal docs are a pain for devs! Apache 2.0 is well-known and tech-friendly, making it easier for non-native developers to understand the implications too.
🔗 Easier Integration: Apache 2.0 is compatible with many other licenses, simplifying tasks like model merging with models of different licensing requirements.
🚫 No Permission Needed: Custom licenses often require explicit permission and additional documentation work of filling forms, creating barriers. Apache 2.0 removes this hurdle, letting devs focus on innovation.
There are a lot interesting discussions from
@JustinLin610 's poll: https://x.com/JustinLin610/status/1793559737482764375 which inspired this thread.
Any other thoughts? Let me know ^^
@01AI_Yi recently switched from a permissive & commercially friendly license, to Apache 2.0. And the community loved it! 🚀
@JustinLin610 also had a poll on model license and the majority votes for Apache 2.0.
Why it is a Big Deal? ⬇️
📚 Legal Simplicity: Custom licenses need costly & time-consuming legal review. Apache 2.0 is well-known & easier for legal teams to handle.
👩💻 Developer-Friendly: Legal docs are a pain for devs! Apache 2.0 is well-known and tech-friendly, making it easier for non-native developers to understand the implications too.
🔗 Easier Integration: Apache 2.0 is compatible with many other licenses, simplifying tasks like model merging with models of different licensing requirements.
🚫 No Permission Needed: Custom licenses often require explicit permission and additional documentation work of filling forms, creating barriers. Apache 2.0 removes this hurdle, letting devs focus on innovation.
There are a lot interesting discussions from
@JustinLin610 's poll: https://x.com/JustinLin610/status/1793559737482764375 which inspired this thread.
Any other thoughts? Let me know ^^
posted
an
update
6 days ago
Post
934
DeepSeekV2 is a big deal. Not only because its significant improvements to both key components of Transformer: the Attention layer and FFN layer.
It has also completed disrupted the Chines LLM market and forcing the competitors to drop the price to 1% of the original price.
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There are two key components in Transformer architecture: the self-attention layer, which captures relationships between tokens in context, and the Feed-Forward Network (FFN) layer, which stores knowledge.
DeepSeek V2 introduces optimizations to both:
Attention layer normally uses KV Cache to reduce repetitive compute, but it consumes significant GPU RAM, limiting concurrent requests. DeepSeek V2 introduces Multi-head Latent Attention (MLA), which stores only a small latent representation, resulting in substantial RAM savings.
DeepSeek V2 utilizes 162 experts instead of the usual 8 as in Mixtral. This approach segments experts into finer granularity for higher specialization and more accurate knowledge acquisition. Activating only a small subset of experts for each token, leads to efficient processing.
It disrupted the market by dropping API prices to $0.14 per 1M tokens. This dramatic reduction forced competitors like GLM, Ernie, and QWen to follow suit, lowering their prices to 1% of their original offerings. Now, users can access these APIs at 1/35th the cost of ChatGPT-4o.
It has also completed disrupted the Chines LLM market and forcing the competitors to drop the price to 1% of the original price.
---
There are two key components in Transformer architecture: the self-attention layer, which captures relationships between tokens in context, and the Feed-Forward Network (FFN) layer, which stores knowledge.
DeepSeek V2 introduces optimizations to both:
Attention layer normally uses KV Cache to reduce repetitive compute, but it consumes significant GPU RAM, limiting concurrent requests. DeepSeek V2 introduces Multi-head Latent Attention (MLA), which stores only a small latent representation, resulting in substantial RAM savings.
DeepSeek V2 utilizes 162 experts instead of the usual 8 as in Mixtral. This approach segments experts into finer granularity for higher specialization and more accurate knowledge acquisition. Activating only a small subset of experts for each token, leads to efficient processing.
It disrupted the market by dropping API prices to $0.14 per 1M tokens. This dramatic reduction forced competitors like GLM, Ernie, and QWen to follow suit, lowering their prices to 1% of their original offerings. Now, users can access these APIs at 1/35th the cost of ChatGPT-4o.
posted
an
update
about 1 month ago
Post
1839
So hard to keep up with pace!!! Lots of new Chinese fine-tunes are being released on HF
So I asked my agent to create a collection
xianbao/llama3-zh-662ba8503bdfe51948a28403
code: https://colab.research.google.com/drive/1ap6fP-VytZE367Nqk26DeQqgQkYaf-cD#scrollTo=eljRbYb4c92M
Would be nice to run then regularly. Any thoughts / suggestions on where to host this cron job?
So I asked my agent to create a collection
xianbao/llama3-zh-662ba8503bdfe51948a28403
code: https://colab.research.google.com/drive/1ap6fP-VytZE367Nqk26DeQqgQkYaf-cD#scrollTo=eljRbYb4c92M
Would be nice to run then regularly. Any thoughts / suggestions on where to host this cron job?
- 1 reply
posted
an
update
4 months ago
Post
Welcome Bunny! A family of lightweight but powerful multimodal models from BAAI
With detailed work on dataset curation, the Bunny-3B model built upon SigLIP and Phi-2 achieves performance on par with 13B models.
Model: BAAI/bunny-phi-2-siglip-lora
With detailed work on dataset curation, the Bunny-3B model built upon SigLIP and Phi-2 achieves performance on par with 13B models.
Model: BAAI/bunny-phi-2-siglip-lora
- 2 replies
posted
an
update
4 months ago
Post
There appears to be a huge misunderstanding regarding the licensing requirements for open sourced Chinese speaking speaking LLMs on
@huggingface
I initially shared this misconception too, but after conducting some research, I came up with the list below.
Veryimpressive!
@huggingface
I initially shared this misconception too, but after conducting some research, I came up with the list below.
Veryimpressive!
::fire::
::fire::
posted
an
update
4 months ago
replied to
osanseviero's
post
4 months ago
Worth trying out!
Learning a lang from another lang family might improve model's capability on appearing unrelated aspects.
The study of French improved my grammar and I wished I could master another language like Arabic / Hindi to see the world from a different angle.