Update README.md

README.md

@@ -8,11 +8,11 @@ license: bigscience-openrail-m
 ---
 
 
-[GeoV](https://huggingface.co/docs/transformers/model_doc/geov)-9B is a 9 billion parameter causal language model.
+[GeoV](https://github.com/geov-ai/geov)-9B is a 9 billion parameter causal language model.
 
 The GeoV model was designed by Georges Harik and uses 
 [Rotary Positional Embeddings with Relative distances (RoPER)](http://research.labml.ai/RoPER.html)
-by [Georges Hark](https://twitter.com/ghark) and [Varuna Jayasiri](https://twitter.com/vpj).
+by [Georges Harik](https://twitter.com/gharik) and [Varuna Jayasiri](https://twitter.com/vpj).
 
 [RoPER]((http://research.labml.ai/RoPER.html), 
 in addition to using relative positions in the attention score calculation by RoPE embeddings,
@@ -43,25 +43,31 @@ The released weights were trained on ~70 billion tokens.
 We plan to continue training up to 300 billion tokens and update the weights at every 20b tokens.
 This training run is monolingual and uses c4en and english wikipedia datasets.
 
+## Installation
+
+```shell
+pip install geov
+```
+
 ## Generation
 
-The `generate()` method can be used to generate text using GeoV model.
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/geov-ai/geov/blob/master/notebooks/generate.ipynb)
 
 ```python
->>> from transformers import GeoVForCausalLM, GeoVTokenizer
+from geov import GeoVForCausalLM, GeoVTokenizer
 
->>> model = GeoVForCausalLM.from_pretrained("GeoV/GeoV-9b")
->>> tokenizer = GeoVTokenizer.from_pretrained("GeoV/GeoV-9b")
+model = GeoVForCausalLM.from_pretrained("GeoV/GeoV-9b")
+tokenizer = GeoVTokenizer.from_pretrained("GeoV/GeoV-9b")
 
->>> prompt = "In mathematics, topology is the study of"
+prompt = "In mathematics, topology is the study of"
 
->>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids
 
->>> gen_tokens = model.generate(
-...     input_ids,
-...     do_sample=True,
-...     temperature=0.9,
-...     max_length=100,
-... )
->>> gen_text = tokenizer.batch_decode(gen_tokens)[0]
+gen_tokens = model.generate(
+    input_ids,
+    do_sample=True,
+    temperature=0.9,
+    max_length=100,
+)
+gen_text = tokenizer.batch_decode(gen_tokens)[0]
 ```