The model is a finetuned version of Genz-13B-v2 with a context size of 16K. The model architecture is updated to have lamda attention from the LM-Infinite paper which gives the model capability of 120K+ sequence length without affecting the preplexity
You can integrate the model in your code my loading convert_llama_model function.
import torch
from transformers import GenerationConfig, AutoModelForCausalLM, AutoTokenizer
from model.llama import convert_llama_model
local_branch = 2048
global_branch = 10
limit_distance = 2048
model = AutoModelForCausalLM.from_pretrained(
"budecosystem/genz-13b-infinite",
torch_dtype=torch.float16,
device_map="auto",
)
model = convert_llama_model(model, local_branch, global_branch)
Evaluation
Task
4096
5120
8192
16384
Passkey retreival
100
75
48
30
Training details
The model is trained of 4 A100 80GB for approximately 55hrs.
Hyperparameters
Value
per_device_train_batch_size
1
gradient_accumulation_steps
1
epoch
3
steps
8550
learning_rate
2e-4
lr schedular type
cosine
warmup steps
1000
optimizer
adamw
fp16
True
GPU
4 A100 80GB
Acknowledgments
We'd like to thank the open-source community and the researchers whose foundational work laid the path to this model. Special shoutout to the authors of
LM-Infinite paper
and the
GitHub repo
Runs of budecosystem genz-13b-infinite on huggingface.co
56
Total runs
0
24-hour runs
0
3-day runs
-2
7-day runs
9
30-day runs
More Information About genz-13b-infinite huggingface.co Model
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