moonshotai/Kimi-K3
Image-Text-to-Text • 2.8T • Updated • 388k • • 8.9k
Safzan Pirani
lilcheaty
AI & ML interests
None yet
Recent Activity
liked a model 7 days ago
moonshotai/Kimi-K3 updated a model about 1 month ago
lilcheaty/LFM2.5-230M-MLX-attn8-base6 published a model about 1 month ago
lilcheaty/LFM2.5-230M-MLX-attn8-base6Organizations
liked a
model 7 days ago
updated a
model about 1 month ago
published a
model about 1 month ago
updated a
model about 1 month ago
published a
model about 1 month ago
updated 2
models 4 months ago
published 2
models 4 months ago
liked a
model about 1 year ago
liked a
Space over 1 year ago
liked a
model over 1 year ago
reacted to vincentg64's post with 👀 almost 2 years ago
Post
1460
Hyperfast Contextual Custom LLM with Agents, Multitokens, Explainable AI, and Distillation https://mltblog.com/4dNPSnB
New additions to this ground-breaking system include multi-token distillation when processing prompts, agents to meet user intent, more NLP, and a command prompt menu accepting both standard prompts and various actions.
I also added several illustrations, featuring xLLM in action with a full session and sample commands to fine-tune in real-time. All the code, input sources (anonymized corporate corpus from fortune 100 company), contextual backend tables including embeddings, are on GitHub. My system has zero weight, no transformer, and no neural network. It relies on explainable AI, does not require training, is fully reproducible, and fits in memory. Yet your prompts can retrieve relevant full text entities from the corpus with no latency — including URLs, categories, titles, email addresses, and so on — thanks to well-designed architecture.
Read more, get the code, paper and everything for free, at https://mltblog.com/4dNPSnB
New additions to this ground-breaking system include multi-token distillation when processing prompts, agents to meet user intent, more NLP, and a command prompt menu accepting both standard prompts and various actions.
I also added several illustrations, featuring xLLM in action with a full session and sample commands to fine-tune in real-time. All the code, input sources (anonymized corporate corpus from fortune 100 company), contextual backend tables including embeddings, are on GitHub. My system has zero weight, no transformer, and no neural network. It relies on explainable AI, does not require training, is fully reproducible, and fits in memory. Yet your prompts can retrieve relevant full text entities from the corpus with no latency — including URLs, categories, titles, email addresses, and so on — thanks to well-designed architecture.
Read more, get the code, paper and everything for free, at https://mltblog.com/4dNPSnB
- 2 replies
updated a
Space over 3 years ago