Create README.md

README.md

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+---
+license: llama2
+datasets:
+- tiiuae/falcon-refinedweb
+- EleutherAI/pile
+- meta-math/MetaMathQA
+language:
+- en
+library_name: transformers
+
+---
+# Saily 220B
+<img src="https://i.ibb.co/rG8S6cF/Saily-220-B.png" style="width: 100%; height: auto;"/>
+
+---
+## Announcements
+**1.** <b>Date: </b>17th December, 2023
+Releasing v1. Saily_220B is a powerful AI model built on top of Llama2-70B merges.
+We created 10 fine-tuned **Llama2 70B** models. The models were were fine-tuned on a part of Refined-Web Dataset (common for all)
+and individually the models were finetuned on niche specific datasets:
+- Code
+- Humor
+- Maths
+- Logical Understanding
+- Physics
+- Reasoning
+- Psychology
+- Roleplay
+
+We created 4 linear merges while keeping **Logical-Understanding** and **Reasoning** models constant in all linear merges.
+and then finally we created a passthrough merge between the models.
+
+Public Datasets used:
+1. [RefinedWeb](https://hf.co/datasets/tiiuae/falcon-refinedweb) (part of it)
+2. Pile (part of it)
+3. [MetaMathQA](https://hf.co/datasets/meta-math/MetaMathQA)
+4. Unnatural Code (Javascript, Python, C++)
+
+### How did we create the private dataset?
+We recorded many internal brain-storming sessions where we just talked about random things.
+We also invited many experts from different fields: 
+ - Mathematicians
+ - Developers
+ - Bio-Engineers
+ - Authors
+ - Psychologists
+ - and others...
+
+We talked about different things with them and recorded the sessions and then transcribed the audio to create the datasets.
+
+---
+
+### Please don't refer to the config.json in the files, it isn't accurate. You can run:
+```python
+from transformers import AutoModelForCausalLM as amclm
+model = amclm.from_pretrained("deepnight-research/saily_220b",
+    device_map="auto")
+
+# print(model.config)
+model.config
+```
+to check out the model's configuration.
+
+---
+
+
+### Try it:
+
+You definitely need GPUs here (that goes without saying)
+* We have tried it on **4 x A100 80GB** and **2 x A100 80GB**.
+* You will have to load the model in **4bit** to fit on **2 x A100 (80GB)**.
+
+```python
+from transformers import AutoModelForCausalLM as amclm
+from transformers import AutoTokenizer
+
+model_name = "deepnight-research/saily_220b"
+model = amclm.from_pretrained(model_name, device_map="auto")
+
+# To load in 8Bit, make sure you have bitsandbytes installed.
+# model = amclm.from_pretrained(model_name,
+#           device_map="auto",
+#           load_in_8bit=True
+#        )
+
+# Float16
+# import torch
+# model = amclm.from_pretrained(model_name,
+#            device_map="auto",
+#            torch_dtype=torch.float16
+#         )
+
+tokenizer = AutoTokenier.from_pretrained(model_name)
+
+input_ids = tokenizer.encode("[INST]\nWrite a poem about cats\n[/INST]\n\n", return_tensors="pt")
+
+output = model.generate(input_ids, max_length=128,
+            temperature=0.7, 
+            repetition_penalty=1.1, 
+            top_p=0.7, top_k=50
+        )
+
+output_text = tokenizer.decode(output[0], skip_special_tokens=True)
+```
+
+We recommend following **Alpaca Prompt Format**, and if you're trying it out in Text-Generation-WebUI, please use **INSTRUCT** or **CHAT-INSTRUCT** mode.
+
+
+---
+
+## Limitations and Bias
+As with all language models, Saily_220B may generate incorrect or biased content. It's important to keep this in mind when using the model.
+
+---
+
+## Wanna Talk?
+Reach out to us at [research@deepnight.tech](mailto:research@deepnight.tech) or [hello@deepnight.tech](mailto:hello@deepnight.tech)