trillionlabs/Trida-7B
Text Generation
•
8B
•
Updated
•
133
•
4
kimgangyeol
kkyy3402
AI & ML interests
None yet
Recent Activity
liked
a model
about 8 hours ago
trillionlabs/Trida-7B
liked
a model
1 day ago
zai-org/GLM-OCR
liked
a model
5 days ago
PaddlePaddle/PaddleOCR-VL-1.5
Organizations
None yet
liked
a
model
about 8 hours ago
liked
a
model
1 day ago
liked
a
model
5 days ago
liked
a
dataset
6 days ago
liked
a
model
16 days ago
liked
2
models
about 1 month ago
liked
a
model
about 2 months ago
liked
a
model
3 months ago
liked
2
models
4 months ago
reacted to
Kseniase's
post with 👍
4 months ago
Post
6183
10 awesome advanced LoRA approaches
Low-Rank Adaptation (LoRA) is the go-to method for efficient model fine-tuning that adds small low-rank matrices instead of retraining full models. The field isn’t standing still – new LoRA variants push the limits of efficiency, generalization, and personalization. So we’re sharing 10 of the latest LoRA approaches you should know about:
1. Mixture-of-LoRA-experts → Mixture of Low-Rank Adapter Experts in Generalizable Audio Deepfake Detection (2509.13878)
Adds multiple low-rank adapters (LoRA) into a model’s layers, and a routing mechanism activates the most suitable ones for each input. This lets the model adapt better to new unseen conditions
2. Amortized Bayesian Meta-Learning for LoRA (ABMLL) → Amortized Bayesian Meta-Learning for Low-Rank Adaptation of Large Language Models (2508.14285)
Balances global and task-specific parameters within a Bayesian framework to improve uncertainty calibration and generalization to new tasks without high memory or compute costs
3. AutoLoRA → AutoLoRA: Automatic LoRA Retrieval and Fine-Grained Gated Fusion for Text-to-Image Generation (2508.02107)
Automatically retrieves and dynamically aggregates public LoRAs for stronger T2I generation
4. aLoRA (Activated LoRA) → Activated LoRA: Fine-tuned LLMs for Intrinsics (2504.12397)
Only applies LoRA after invocation, letting the model reuse the base model’s KV cache instead of recomputing the full turn’s KV cache. Efficient in multi-turn conversations
5. LiLoRA (LoRA in LoRA) → LoRA in LoRA: Towards Parameter-Efficient Architecture Expansion for Continual Visual Instruction Tuning (2508.06202)
Shares the LoRA matrix A across tasks and additionally low-rank-decomposes matrix B to cut parameters in continual vision-text MLLMs
6. Sensitivity-LoRA → Sensitivity-LoRA: Low-Load Sensitivity-Based Fine-Tuning for Large Language Models (2509.09119)
Dynamically assigns ranks to weight matrices based on their sensitivity, measured using second-order derivatives
Read further below ↓
Also, subscribe to the Turing Post: https://www.turingpost.com/subscribe
Low-Rank Adaptation (LoRA) is the go-to method for efficient model fine-tuning that adds small low-rank matrices instead of retraining full models. The field isn’t standing still – new LoRA variants push the limits of efficiency, generalization, and personalization. So we’re sharing 10 of the latest LoRA approaches you should know about:
1. Mixture-of-LoRA-experts → Mixture of Low-Rank Adapter Experts in Generalizable Audio Deepfake Detection (2509.13878)
Adds multiple low-rank adapters (LoRA) into a model’s layers, and a routing mechanism activates the most suitable ones for each input. This lets the model adapt better to new unseen conditions
2. Amortized Bayesian Meta-Learning for LoRA (ABMLL) → Amortized Bayesian Meta-Learning for Low-Rank Adaptation of Large Language Models (2508.14285)
Balances global and task-specific parameters within a Bayesian framework to improve uncertainty calibration and generalization to new tasks without high memory or compute costs
3. AutoLoRA → AutoLoRA: Automatic LoRA Retrieval and Fine-Grained Gated Fusion for Text-to-Image Generation (2508.02107)
Automatically retrieves and dynamically aggregates public LoRAs for stronger T2I generation
4. aLoRA (Activated LoRA) → Activated LoRA: Fine-tuned LLMs for Intrinsics (2504.12397)
Only applies LoRA after invocation, letting the model reuse the base model’s KV cache instead of recomputing the full turn’s KV cache. Efficient in multi-turn conversations
5. LiLoRA (LoRA in LoRA) → LoRA in LoRA: Towards Parameter-Efficient Architecture Expansion for Continual Visual Instruction Tuning (2508.06202)
Shares the LoRA matrix A across tasks and additionally low-rank-decomposes matrix B to cut parameters in continual vision-text MLLMs
6. Sensitivity-LoRA → Sensitivity-LoRA: Low-Load Sensitivity-Based Fine-Tuning for Large Language Models (2509.09119)
Dynamically assigns ranks to weight matrices based on their sensitivity, measured using second-order derivatives
Read further below ↓
Also, subscribe to the Turing Post: https://www.turingpost.com/subscribe
- 3 replies
liked
5
models
5 months ago
apple/FastVLM-0.5B
Text Generation
•
0.8B
•
Updated
•
14.4k
•
368
google/embeddinggemma-300m
Sentence Similarity
•
0.3B
•
Updated
•
1M
•
•
1.45k
trillionlabs/Tri-1.8B-Translation
Translation
•
2B
•
Updated
•
1.31k
•
18
kkresearch/bge-reranker-v2-m3-korean-finance
Text Generation
•
0.6B
•
Updated
•
2
dev7halo/Qwen3-32B-Q5_K_M-GGUF
Text Generation
•
33B
•
Updated
•
65
•
1
upvoted
2
papers
5 months ago
liked
a
model
5 months ago
