license: mit
language:
- en
thumbnail: >-
https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/TqnMpteVAyfiiNHx4lVkU.png
You are welcome here, traveler.
Named after the method used to create it, interleaving the layers of its predecessor to become far larger, giving it much more potential.
Elothir was an ancient treeant, and I couldn't think of a better name for a model that was created using the passthrough method.
The passthrough method differs significantly from the previous ones. By concatenating layers from different LLMs, it can produce models with an exotic number of parameters (e.g., 9B with two 7B parameter models). These models are often referred to as "frankenmerges" or "Frankenstein models" by the community.
Many thanks to Abacaj for providing the fine tuned weights that were used in the creation of this base model. You can find the full script for how the model was merged [here]...thanks to KatyTheCutie for helping me figure out how to make the model as big as I possibly could.
This idea was brought to me by The Face of Goonery, also known as Caleb Morgan. I have him to thank if fine-tuning this model turns out to be a success
How to run inference:
import transformers
import torch
if __name__ == "__main__":
model_name = "abacaj/phi-2-super"
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
model = (
transformers.AutoModelForCausalLM.from_pretrained(
model_name,
)
.to("cuda:0")
.eval()
)
messages = [
{"role": "user", "content": "Hello, who are you?"}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
input_ids_cutoff = inputs.size(dim=1)
with torch.no_grad():
generated_ids = model.generate(
input_ids=inputs,
use_cache=True,
max_new_tokens=512,
temperature=0.2,
top_p=0.95,
do_sample=True,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
)
completion = tokenizer.decode(
generated_ids[0][input_ids_cutoff:],
skip_special_tokens=True,
)
print(completion)
Chat template
The model uses the same chat template as found in Mistral instruct models:
text = "<|endoftext|>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!<|endoftext|> "
"[INST] Do you have mayonnaise recipes? [/INST]"
You don't need to do it manually if you use the HF transformers tokenizer:
messages = [
{"role": "user", "content": "Hello, who are you?"},
{"role": "assistant": "content": "I am ..."}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)