Downloads · 30 days
49
0% of all-time downloads
budecosystem/genz-13b-infinite
genz-13b-infinite is a text generation model from budecosystem. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as llama2.
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 lengt…
Downloads · 30 days
49
0% of all-time downloads
All-time downloads
30.8K
Public
Repo size
52.1 GB
Likes
2
Public
Click a slice to open those files.
.bin26 GB · 100%
From the Hugging Face model README
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
Use the generate.py file from the github repo
python generate.py --base_model budecosystem/genz-13b-infinite
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)
| Task | 4096 | 5120 | 8192 | 16384 |
|---|---|---|---|---|
| Passkey retreival | 100 | 75 | 48 | 30 |
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 |
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