| Name | Version | Summary | date |
| meshgpt-pytorch |
1.2.8 |
MeshGPT Pytorch |
2024-05-17 19:34:46 |
| optimum-benchmark |
0.2.1 |
Optimum-Benchmark is a unified multi-backend utility for benchmarking Transformers, Timm, Diffusers and Sentence-Transformers with full support of Optimum's hardware optimizations & quantization schemes. |
2024-05-17 09:25:34 |
| spacyrerank |
0.0.6 |
Rank phrases and text based on query by leveraging hugging-face models. |
2024-05-17 06:44:03 |
| querent |
3.1.1 |
The Asynchronous Data Dynamo and Graph Neural Network Catalyst |
2024-05-13 07:21:27 |
| x-transformers |
1.30.1 |
X-Transformers - Pytorch |
2024-05-12 14:29:22 |
| gliner |
0.1.13 |
Generalist model for NER (Extract any entity types from texts) |
2024-05-11 10:45:02 |
| iTransformer |
0.6.0 |
iTransformer - Inverted Transformer Are Effective for Time Series Forecasting |
2024-05-10 14:33:23 |
| infini-transformer-pytorch |
0.1.1 |
Infini-Transformer in Pytorch |
2024-05-09 14:30:15 |
| q-transformer |
0.1.15 |
Q-Transformer |
2024-05-07 17:21:23 |
| self-reasoning-tokens-pytorch |
0.0.4 |
Self Reasoning Tokens |
2024-05-05 18:01:34 |
| sibila |
0.4.2 |
Structured queries from local or online LLM models |
2024-05-04 18:40:09 |
| soundstorm-pytorch |
0.4.3 |
SoundStorm - Efficient Parallel Audio Generation from Google Deepmind, in Pytorch |
2024-05-04 13:15:38 |
| audiolm-superfeel |
2.1.7 |
AudioLM - Language Modeling Approach to Audio Generation from Google Research - Pytorch |
2024-05-04 12:58:19 |
| soundstorm-superfeel |
0.4.5 |
SoundStorm - Efficient Parallel Audio Generation from Google Deepmind, in Pytorch |
2024-05-04 10:33:55 |
| MEGABYTE-pytorch |
0.3.0 |
MEGABYTE - Pytorch |
2024-05-03 02:14:25 |
| autoawq |
0.2.5 |
AutoAWQ implements the AWQ algorithm for 4-bit quantization with a 2x speedup during inference. |
2024-05-02 18:32:41 |
| datadreamer.dev |
0.35.0 |
Prompt. Generate Synthetic Data. Train & Align Models. |
2024-05-02 05:36:30 |
| REaLTabFormer |
0.1.7 |
A novel method for generating tabular and relational data using language models. |
2024-04-28 18:00:11 |
| s5-pytorch |
0.2.1 |
S5 - Simplified State Space Layers for Sequence Modeling - Pytorch |
2024-04-26 09:39:13 |
| nncf |
2.10.0 |
Neural Networks Compression Framework |
2024-04-25 12:01:53 |