CohereLabs/BLS-Mini-Code-1.0
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Hugging Face MCP Server v0.3.17
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SEP-2640 "Skills Over MCP" support added (early access)
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SEP-2640 "Skills Over MCP" support added (early access)
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#1 opened 10 days ago
by djuna
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96% Correct Next Token Prediction, with No DNN, no Training, auto-distilled model - https://mltblog.com/4urfvTB
Over the last 12 months, I’ve built a model to predict the next token and to suggest synonyms or related queries to a user prompt, with 100% correct predictions on the training set in one shot, without training or deep neural networks (DNNs). The same model is now integrated in some of the most recent LLM architectures, albeit with costly training via DNNs. My version does not need DNNs or training.
The purpose of this article is to provide validation to my deep neural network alternative in the context of LLMs. The new model is as a substitute to standard DNNs, with increased explainability and higher accuracy. It is designed for corporate corpuses. The end goal is to provide better accuracy at a much lower cost, while providing full control over all the components.
An interesting feature is auto-distillation, whereas the model self-identifies weights that do not contribute over time in 99.9% of user-generated prompts, and drop them, based on prompts from a large, specialized user base. The gain is most spectacular in open-weight LLMs applied to specialized contexts, whether based on DNNs or not.
Read article and download the free technical paper with NVIDIA case study, at https://mltblog.com/4urfvTB
Over the last 12 months, I’ve built a model to predict the next token and to suggest synonyms or related queries to a user prompt, with 100% correct predictions on the training set in one shot, without training or deep neural networks (DNNs). The same model is now integrated in some of the most recent LLM architectures, albeit with costly training via DNNs. My version does not need DNNs or training.
The purpose of this article is to provide validation to my deep neural network alternative in the context of LLMs. The new model is as a substitute to standard DNNs, with increased explainability and higher accuracy. It is designed for corporate corpuses. The end goal is to provide better accuracy at a much lower cost, while providing full control over all the components.
An interesting feature is auto-distillation, whereas the model self-identifies weights that do not contribute over time in 99.9% of user-generated prompts, and drop them, based on prompts from a large, specialized user base. The gain is most spectacular in open-weight LLMs applied to specialized contexts, whether based on DNNs or not.
Read article and download the free technical paper with NVIDIA case study, at https://mltblog.com/4urfvTB
upvoted an article 12 days ago
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Harness, Scaffold, and the AI Agent Terms Worth Getting Right
sergiopaniego, ariG23498
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Greetings Hugging Face!
I started a new project called **FWKV** (Feed-forward Weighted Key Value, or Floored Weighted Key Value), a RWKV-style LM that uses FFNNs (Feed-Forward Neural Networks) instead of RNN and
So far I have:
- FlameF0X/FWKV-29M — this one is undertrained and doesn't have a Space yet. In the attached image you can see its speed on a T4 compared to models with the same configuration.
The only model that's fully working right now is:
- FlameF0X/FWKV-TinyStories — trained on TinyStories for one epoch. The demo Space is FlameF0X/FWKV-demo.
I started a new project called **FWKV** (Feed-forward Weighted Key Value, or Floored Weighted Key Value), a RWKV-style LM that uses FFNNs (Feed-Forward Neural Networks) instead of RNN and
floor(W·K·V). I'm hoping to make it much more efficient and scalable than RWKV.So far I have:
- FlameF0X/FWKV-29M — this one is undertrained and doesn't have a Space yet. In the attached image you can see its speed on a T4 compared to models with the same configuration.
The only model that's fully working right now is:
- FlameF0X/FWKV-TinyStories — trained on TinyStories for one epoch. The demo Space is FlameF0X/FWKV-demo.
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