Create README.md

README.md

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+---
+datasets:
+- IlyaGusev/habr
+- Den4ikAI/russian_instructions
+- wiki_qa
+inference:
+  parameters:
+    max_new_tokens: 32
+    temperature: 1
+    top_k: 50
+    top_p: 0.7
+    do_sample: true
+license: apache-2.0
+language:
+- ru
+pipeline_tag: text-generation
+widget:
+- text: Чем отличается лось от ежа?
+  example_title: Question Answering
+- text: Как выпросить повышение?
+  example_title: Logical reasoning
+- text: Какая температура закипания азота?
+  example_title: Scientific knowledge
+library_name: transformers
+tags:
+- finance
+- code
+---
+
+<h1 style="font-size: 42px">Instructions ruGPT Small v0.1a<h1/>
+
+
+
+# Model Summary
+
+> Я дообучил small rugpt на датасете инструкций, хабра, QA и кода
+
+
+# Quick Start
+
+```python
+from transformers import pipeline
+pipe = pipeline(model='AlexWortega/instruct_rugptSmall')
+pipe('''Как собрать питон код?''')
+```
+or
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("AlexWortega/instruct_rugptSmall")
+model = AutoModelForCausalLM.from_pretrained("AlexWortega/instruct_rugptSmall")
+```
+
+# License
+
+The weights of Instructions ruGPT Small v0.1a are licensed under version 2.0 of the Apache License.
+
+
+
+## Hyperparameters
+
+I used Novograd with a learning rate of 2e-5 and global batch size of 6 (3 for each data parallel worker).
+I use both data parallelism and pipeline parallelism to conduct training.
+During training, we truncate the input sequence to 1024 tokens, and for input sequence that contains less than 1024 tokens, we concatenate multiple sequences into one long sequence to improve the data efficiency.
+
+
+
+# References
+
+#Metrics
+
+SOON
+
+## BibTeX entry and citation info
+
+```bibtex
+@article{
+  title={GPT2xl is underrated task solver},
+  author={Nickolich Aleksandr, Karina Romanova, Arseniy Shahmatov, Maksim Gersimenko},
+  year={2023}
+}
+```