Update README.md

README.md

@@ -6,6 +6,8 @@ language:
 
 # GPT-NeoXT-Chat-Base-20B
 
+***<p style="font-size: 24px">Feel free to try out our [OpenChatKit feedback app](https://huggingface.co/spaces/togethercomputer/OpenChatKit)!</p>***
+
 > TLDR: As part of OpenChatKit (codebase available [here](https://github.com/togethercomputer/OpenChaT)),
 > GPT-NeoXT-Chat-Base-20B is a 20B parameter language model, fine-tuned from EleutherAI’s GPT-NeoX with over 40 million instructions on 100% carbon negative compute.
 
@@ -23,6 +25,20 @@ You can read more about this process and the availability of this dataset in LAI
 - **Model Description**: A 20B parameter open source chat model, fine-tuned from EleutherAI’s NeoX with over 40M instructions on 100% carbon negative compute
 - **Resources for more information**: [GitHub Repository](https://github.com/togethercomputer/OpenChaT).
 
+# Quick Start
+
+```python
+from transformers import pipeline
+pipe = pipeline(model='togethercomputer/GPT-NeoXT-Chat-Base-20B')
+pipe('''<human>: Hello!\n<bot>:''')
+```
+or
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("togethercomputer/GPT-NeoXT-Chat-Base-20B")
+model = AutoModelForCausalLM.from_pretrained("togethercomputer/GPT-NeoXT-Chat-Base-20B")
+```
+
 ## Strengths of the model
 
 There are several tasks that OpenChatKit excels at out of the box. This includes: 
@@ -160,7 +176,8 @@ We therefore welcome contributions from individuals and organizations, and encou
 ## Training
 
 **Training Data**
-\[TODO\]
+
+Please refer to [togethercomputer/OpenDataHub](https://github.com/togethercomputer/OpenDataHub)
 
 **Training Procedure**
 
@@ -170,15 +187,4 @@ We therefore welcome contributions from individuals and organizations, and encou
 - **Batch:** 2 x 2 x 64 x 2048 = 524288 tokens
 - **Learning rate:** warmup to 1e-6 for 100 steps and then kept constant
 
-## Environmental Impact
-\[TODO\]
-**Stable Diffusion v1** **Estimated Emissions**
-Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
-
-- **Hardware Type:** A100 PCIe 40GB
-- **Hours used:** 200000
-- **Cloud Provider:** AWS
-- **Compute Region:** US-east
-- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq.
-