This model is the 2nd extracted standalone model from the
mistralai/Mixtral-8x7B-v0.1
, using the
Mixtral Model Expert Extractor tool
I made. It is constructed by selecting the first expert from each Mixture of Experts (MoE) layer. The extraction of this model is experimental. It is expected to be worse than Mistral-7B.
Model Architecture
The architecture of this model includes:
Multi-head attention layers derived from the base Mixtral model.
The first expert from each MoE layer, intended to provide a balanced approach to language understanding and generation tasks.
Additional layers and components as required to ensure the model's functionality outside the MoE framework.
Example
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "DrNicefellow/Mistral-2-from-Mixtral-8x7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
text = "Today is a pleasant"
input_ids = tokenizer.encode(text, return_tensors='pt')
output = model.generate(input_ids)
print(tokenizer.decode(output[0], skip_special_tokens=True))
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