license: apache-2.0
library_name: transformers
Laser-Dolphin-Mixtral-2x7b-dpo
Credit to Fernando Fernandes and Eric Hartford for their project laserRMT
This model is a medium-sized MoE implementation based on cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
A 2x7b configuration offers better performance than a standard 7b model even if loaded in 4 bit. (9G VRAM)
If this 2x7b model is loaded in 4 bit the hellaswag score is .8270 which is higher than the base model achieves on its own in full precision.
The process is outlined in this notebook
These Quants will result in unpredicted behavior and I am working on new Quants as I have updated the model
Quatizations provided by TheBloke
Code Example
Switch the commented model definition to use in 4-bit. Should work with 9GB and still exceed the single 7B model by 5-6 points roughly
from transformers import AutoModelForCausalLM, AutoTokenizer
def generate_response(prompt):
"""
Generate a response from the model based on the input prompt.
Args:
prompt (str): Prompt for the model.
Returns:
str: The generated response from the model.
"""
# Tokenize the input prompt
inputs = tokenizer(prompt, return_tensors="pt")
# Generate output tokens
outputs = model.generate(**inputs, max_new_tokens=256, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id)
# Decode the generated tokens to a string
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Load the model and tokenizer
model_id = "macadeliccc/piccolo-2x7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True)
prompt = "Write a quicksort algorithm in python"
# Generate and print responses for each language
print("Response:")
print(generate_response(prompt), "\n")
colab with usage example
Eval
TODO
evaluation colab
Citations
Fernando Fernandes Neto and Eric Hartford. "Optimizing Large Language Models Using Layer-Selective Rank Reduction and Random Matrix Theory." 2024.
@article{sharma2023truth,
title={The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction},
author={Sharma, Pratyusha and Ash, Jordan T and Misra, Dipendra},
journal={arXiv preprint arXiv:2312.13558},
year={2023} }
@article{gao2021framework,
title={A framework for few-shot language model evaluation},
author={Gao, Leo and Tow, Jonathan and Biderman, Stella and Black, Sid and DiPofi, Anthony and Foster, Charles and Golding, Laurence and Hsu, Jeffrey and McDonell, Kyle and Muennighoff, Niklas and others},
journal={Version v0. 0.1. Sept},
year={2021}
}