Comments (3)
Hi, what do you mean by "very bad results"? As also discussed in #7, pruning LLaMa seems to be more challenging than pruning e.g. OPT, possibly because it is more parameter efficient. I just ran --sparsity .5
on the 7B model with pretty recent package versions (transformers==4.31.0
, datasets==2.13.1
and torch==2.0.1
) and got 7.20 PPL for Wiki and 9.29 for C4 PPL (some package version newer than the ones we list in the README seems to have broken PTB numbers in general, not sure why). What numbers do you get?
from sparsegpt.
Hello, I make evaluations on some standart LLM evaluation tasks, using "LLM Evaluation Harness":
https://github.com/EleutherAI/lm-evaluation-harness
I get the following results for LLaMA:
LLaMA-7B | Dense | Magnitude 50% | SparseGPT 50% | SparseGPT 2:4 |
---|---|---|---|---|
arc_challenge (acc_norm) | 0.4138 | 0.302 | 0.2833 | 0.291 |
arc_easy (acc_norm) | 0.5248 | 0.2702 | 0.2588 | 0.266 |
boolq (acc) | 0.7315 | 0.6214 | 0.6193 | 0.3823 |
Normally I would expect some performance drop yet for comparison here are the results for BLOOM-7B:
BLOOM-7B1 | Dense | Magnitude 50% | SparseGPT 50% | SparseGPT 2:4 |
---|---|---|---|---|
arc_challenge (acc_norm) | 0.3336 | 0.3072 | 0.3055 | 0.2722 |
arc_easy (acc_norm) | 0.5728 | 0.5261 | 0.5316 | 0.4945 |
boolq (acc) | 0.6291 | 0.6064 | 0.6303 | 0.6226 |
So probably there is something wrong with the implementation, as I mentioned my own implementation also get the same results, so I would like to compare it with your results. Can you please do the experiments with the latest version of transformers
so we can validate?
from sparsegpt.
Sorry I've closed the issue by accident, I would be glad if you can re-open so we can solve the issue. I also tried the experiment with multiple torch, python and transformers versions, if your results are better, I would expect that it works for a very spesific version of libraries for some reason.
from sparsegpt.
Related Issues (20)
- How should I verify the speedup effect of the algorithm? HOT 4
- Purpose of this update
- finetuning sparsified LLaMa HOT 5
- Inference Speedup HOT 3
- Would sparsegpt be available for Llama2? HOT 3
- When would the code for GPT-J-6B be released?
- Adaptation for Pruning Conv2d or Conv3d Layers? HOT 1
- Can SparseGPT be used on BERT ?
- Using llama.py silently fails and occasionally causes system instability
- transformers version is not correct
- Mistral Support HOT 2
- how to use for Baichuan?
- 2:4 sparsity with to_sparse_semi_structured method from pytorch results in memory issue
- Why Hessian can get by activation ($H = XX^T$) ? HOT 4
- Why transpose the input when in case of nn.Linear or nn.Conv1d?
- AttributeError: 'NoneType' object has no attribute 'shape' HOT 7
- AWQ alongside sparsegpt
- why i can't reproduce the result of paper? HOT 7
- [feature request] all models.
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from sparsegpt.