Hugging Face
Models
Datasets
Spaces
Community
Docs
Enterprise
Pricing
Log In
Sign Up
12.8
TFLOPS
1
17
187
Wassim Trabelsi
PRO
wath5
Follow
fireheat's profile picture
Gargaz's profile picture
kenky's profile picture
11 followers
Β·
32 following
wassim-trabelsi
AI & ML interests
NLP, CV
Recent Activity
liked
a Space
4 days ago
multimodalart/nano-banana
reacted
to
codelion
's
post
with π₯
4 days ago
I wanted to share a technique that's been working really well for recovering performance after INT4 quantization. Typically, quantizing the LLM to INT4 (unlike say INT8) for inference can incur some accuracy loss. Instead of accepting the quality loss, we used the FP16 model as a teacher to train a tiny LoRA adapter (rank=16) for the quantized model. The cool part: the model generates its own training data using the Magpie technique so no external datasets needed. This is critical because we want to remain as much as possible in the distribution of the model's natural responses. Last year Apple's foundational models paper (https://arxiv.org/pdf/2407.21075) had proposed a similar technique and found "By using accuracy-recovery LoRA adapters with only rank 16, Alpaca win rate can be improved by 7-18%, GMS8K accuracy is boosted by 5-10%." (page 47). We saw similar results on Qwen3-0.6B: Perplexity: 2.40 β 2.09 (only 5.7% degradation from FP16 baseline) Memory: Only 0.28GB vs 1.0GB for FP16 (75% reduction) Speed: 3.0x faster inference than FP16 Quality: Generates correct, optimized code solutions - Pre-trained adapter: https://huggingface.co/codelion/Qwen3-0.6B-accuracy-recovery-lora - GitHub repo: https://github.com/codelion/ellora Happy to answer questions about the implementation or help anyone trying to replicate this. The key insight is that quantization errors are systematic and learnable - a small adapter can bridge the gap without negating the benefits of quantization. Has anyone else experimented with self-distillation for quantization recovery? Would love to hear about different approaches!
upvoted
an
article
3 months ago
Training and Finetuning Embedding Models with Sentence Transformers v3
View all activity
Organizations
wath5
's models
2
Sort:Β Recently updated
wath5/kgl_lmsys_pref_classif
Text Classification
β’
3B
β’
Updated
Nov 20, 2024
β’
1
wath5/llemma7b-GGUF
7B
β’
Updated
Apr 3, 2024
β’
6