Saily_220B

Saily 220B

<img src="https://i.ibb.co/rG8S6cF/Saily-220-B.png" style="width: 100%; height: auto;"/>

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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 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 (part of it)
2. Pile (part of it)
3. 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.

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Please don't refer to the config.json in the files, it isn't accurate. You can run:

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.

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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).

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.

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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.

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Wanna Talk?

Reach out to us at [email protected] or [email protected]