Update README.md

README.md

@@ -1,3 +1,63 @@
 ---
 license: mit
+datasets:
+- DarwinAnim8or/greentext
+language:
+- en
+tags:
+- fun
+- greentext
+widget:
+- text: ">be me"
+  example_title: "be me"
+- text: ">be zoo keeper"
+co2_eq_emissions:
+  emissions: 30
+  source: "https://mlco2.github.io/impact/#compute"
+  training_type: "fine-tuning"
+  geographical_location: "Oregon, USA"
+  hardware_used: "1x T4, Google Colab"
 ---
+
+# GPT-Greentext-1.5b
+A finetuned version of [GPT2-XL](https://huggingface.co/gpt2-xl) on the 'greentext' dataset.
+A demo is available [here](https://huggingface.co/spaces/DarwinAnim8or/GPT-Greentext-Playground)
+The demo playground is recommended over the inference box on the right. 
+
+This is the largest release of the "GPT-Greentext" model series. The other models can be found here:
+* [355m size model](https://huggingface.co/DarwinAnim8or/GPT-Greentext-355m)
+* [125m size model](https://huggingface.co/DarwinAnim8or/GPT-Greentext-125m)
+
+# Training Procedure
+This was trained on the 'greentext' dataset, on Google Colab.
+This model was trained for 1 epoch with learning rate 1e-2.
+Notably this uses the "prompt" and "completion" style jsonl file, rather than the plain text file found in the greentext dataset.
+This nets somewhat better, mostly more consistent results. 
+
+# Biases & Limitations
+This likely contains the same biases and limitations as the original GPT2 that it is based on, and additionally heavy biases from the greentext dataset.
+It should be noted that offensive or not PG-output is definitely possible and likely will happen.
+
+# Intended Use
+This model is meant for fun, nothing else.
+
+# Noteworthy differences between this model and the others
+This model tends to like no_repeat_ngram_size values of 1 or 2; whereas the other models in this series tend to prefer 3. 
+
+# Sample Use
+```python
+#Import model:
+from happytransformer import HappyGeneration
+happy_gen = HappyGeneration("GPT2", "DarwinAnim8or/GPT-Greentext-1.5b")
+
+#Set generation settings:
+from happytransformer import GENSettings
+args_top_k = GENSettingsGENSettings(no_repeat_ngram_size=1, do_sample=True, top_k=80, temperature=0.8, max_length=150, early_stopping=False)
+
+#Generate a response:
+result = happy_gen.generate_text(""">be me
+>""", args=args_top_k)
+
+print(result)
+print(result.text)
+```