sometimesanotion/Qwen2.5-14B-Vimarckoso-v3
Text Generation
•
Updated
•
307
•
7
sometimesanotion PRO
sometimesanotion
AI & ML interests
Agentic LLM services, model merging, finetunes, distillation
Recent Activity
updated
a model
about 5 hours ago
sometimesanotion/Qwen2.5-14B-Vimarckoso-v3
new activity
about 6 hours ago
hotmailuser/QwenSlerp2-14B:This should be an interesting merge
updated
a model
about 6 hours ago
sometimesanotion/Lamarck-14B-v0.4-Qwenvergence
Organizations
sometimesanotion's activity
updated
a
model
about 5 hours ago
This should be an interesting merge
#1 opened about 8 hours ago
by
sometimesanotion
updated
a
model
about 6 hours ago
liked
a
model
about 9 hours ago
updated
a
model
about 19 hours ago
reacted to
singhsidhukuldeep's
post with 👍
about 20 hours ago
Post
1766
Groundbreaking Research Alert: Rethinking RAG with Cache-Augmented Generation (CAG)
Researchers from National Chengchi University and Academia Sinica have introduced a paradigm-shifting approach that challenges the conventional wisdom of Retrieval-Augmented Generation (RAG).
Instead of the traditional retrieve-then-generate pipeline, their innovative Cache-Augmented Generation (CAG) framework preloads documents and precomputes key-value caches, eliminating the need for real-time retrieval during inference.
Technical Deep Dive:
- CAG preloads external knowledge and precomputes KV caches, storing them for future use
- The system processes documents only once, regardless of subsequent query volume
- During inference, it loads the precomputed cache alongside user queries, enabling rapid response generation
- The cache reset mechanism allows efficient handling of multiple inference sessions through strategic token truncation
Performance Highlights:
- Achieved superior BERTScore metrics compared to both sparse and dense retrieval RAG systems
- Demonstrated up to 40x faster generation times compared to traditional approaches
- Particularly effective with both SQuAD and HotPotQA datasets, showing robust performance across different knowledge tasks
Why This Matters:
The approach significantly reduces system complexity, eliminates retrieval latency, and mitigates common RAG pipeline errors. As LLMs continue evolving with expanded context windows, this methodology becomes increasingly relevant for knowledge-intensive applications.
Researchers from National Chengchi University and Academia Sinica have introduced a paradigm-shifting approach that challenges the conventional wisdom of Retrieval-Augmented Generation (RAG).
Instead of the traditional retrieve-then-generate pipeline, their innovative Cache-Augmented Generation (CAG) framework preloads documents and precomputes key-value caches, eliminating the need for real-time retrieval during inference.
Technical Deep Dive:
- CAG preloads external knowledge and precomputes KV caches, storing them for future use
- The system processes documents only once, regardless of subsequent query volume
- During inference, it loads the precomputed cache alongside user queries, enabling rapid response generation
- The cache reset mechanism allows efficient handling of multiple inference sessions through strategic token truncation
Performance Highlights:
- Achieved superior BERTScore metrics compared to both sparse and dense retrieval RAG systems
- Demonstrated up to 40x faster generation times compared to traditional approaches
- Particularly effective with both SQuAD and HotPotQA datasets, showing robust performance across different knowledge tasks
Why This Matters:
The approach significantly reduces system complexity, eliminates retrieval latency, and mitigates common RAG pipeline errors. As LLMs continue evolving with expanded context windows, this methodology becomes increasingly relevant for knowledge-intensive applications.
liked
a
model
about 20 hours ago
updated
a
collection
5 days ago
liked
2
models
6 days ago
Thanks for mentioning the individual input models
6
#1 opened 8 days ago
by
CultriX
14B model detected as 7B
7
#1049 opened 14 days ago
by
djuna
liked
a
Space
6 days ago
What is the prompt template format?
1
#2 opened 7 days ago
by
nyonna
updated
2
models
7 days ago
Thanks for mentioning the individual input models
6
#1 opened 8 days ago
by
CultriX
liked
2
models
9 days ago