victordiao commited on
Commit
cde761c
1 Parent(s): 124c0fe

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +64 -1
README.md CHANGED
@@ -1,3 +1,66 @@
1
  ---
2
- license: apache-2.0
3
  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
+ inference: false
3
  ---
4
+
5
+ # Robin Model Card
6
+
7
+ ## Model Details
8
+
9
+ Robin is a series of models finetuned from LLaMA on several high-quality data.
10
+
11
+ - **Developed by:** [LMFlow](https://github.com/OptimalScale/LMFlow/)
12
+ - **Model type:** An auto-regressive language model based on the transformer architecture.
13
+ - **License:** Non-commercial license
14
+ - **Finetuned from model:** [LLaMA](https://arxiv.org/abs/2302.13971).
15
+
16
+ ### Model Sources
17
+
18
+ - **Repository:** https://github.com/OptimalScale/LMFlow/
19
+ - **Blog:** https://medium.com/@hkust.ml/robin-v2-launches-achieves-unparalleled-performance-on-openllm-4f6886e822c1
20
+ - **Paper:** https://arxiv.org/abs/2306.12420
21
+ - **Demo:** https://lmflow.com/
22
+
23
+ ## Uses
24
+
25
+ Robin is primarily utilized for conducting research on extensive language models and chatbots, catering to users specializing in natural language processing, machine learning, and artificial intelligence research.
26
+
27
+ ## How to Get Started with the Model
28
+
29
+ We provide four kinds of demos including:
30
+
31
+ - Online Service: If you don't want to run any code and just want to try our models, we deploy our instruction-tuned LLaMA you to have a try.
32
+ - Colab Chatbot (shell): An interactive shell-based chatbot for you to easily deploy a chatbot on colab.
33
+ - Colab Chatbot (web): An interactive web-based chatbot for you to easily deploy your own chatbot on colab.
34
+ - Local Deploy: We also provide a way for you to deploy your model/chatbot locally, which means you can deploy much larger model than previous three methods if you have enough resource.
35
+
36
+ Please refer to https://github.com/OptimalScale/LMFlow#demos
37
+
38
+ ## Training Details
39
+
40
+
41
+ Expanding upon the initial idea of self-instruct techniques, we incorporated several different data sources and build a new dataset called [LMFlow Dataset](http://lmflow.org:5000/lmflow_data.tar.gz).
42
+ The new training split is created by merging the following datasets:
43
+ - ShareGPT: randomly sample 50K English data and 10K Chinese data from ShareGPT.
44
+ - GPT-4-LLM: 52K English data from GPT-4-LLM.
45
+ - BELLE: randomly sample 80K Chinese data from BELLE.
46
+
47
+ See more details in the "Instruction Tuning" section in our [paper](https://arxiv.org/pdf/2306.12420.pdf).
48
+
49
+ ## Evaluation
50
+
51
+ Robin is evaluated with [LMFlow Benchmark](https://blog.gopenai.com/lmflow-benchmark-an-automatic-evaluation-framework-for-open-source-llms-ef5c6f142418).
52
+ See more details in this [paper](https://arxiv.org/pdf/2306.12420.pdf).
53
+
54
+ ## Citation
55
+ If you find this repository useful, please consider giving ⭐ and citing our [paper](https://arxiv.org/abs/2306.12420):
56
+
57
+ ```
58
+ @misc{lmflow,
59
+ author = {Shizhe Diao and Rui Pan and Hanze Dong and KaShun Shum and Jipeng Zhang and Wei Xiong and Tong Zhang},
60
+ title = {LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models},
61
+ year = {2023},
62
+ publisher = {GitHub},
63
+ journal = {GitHub repository},
64
+ howpublished = {\url{https://optimalscale.github.io/LMFlow/}},
65
+ }
66
+ ```