This is the dataset to the model used to train gorilla 7b but in the alpaca format, for lora training. 
Thank you to microsoft and uc berkly for open sourcing these datasets. As of now I do not believe this dataset works, will have to do more testing, but gorilla team plans to realease training code which might make it easer to see how this was fully done. and how it can be done with lora.

For ALPACA LORA users:
Modules you can target with lora:"gate_proj", "down_proj", "up_proj", "q_proj", "v_proj", "k_proj", "o_proj" 
Most lora models use:"q_proj", "v_proj", "k_proj", "o_proj" 
Platypus which got terrific results: "gate_proj", "down_proj", "up_proj"

Research on targeting certain modules still needs to be done, but if you don't want to train over a previously trained models newly learned abilities, target different modules than the ones used for original training.

Hyper perameters used by Platypus:
Hyperparameters for 13B and 70B Models
Hyperparameter Platypus2-13B / 70B
batch size 16
micro batch size 1
num epochs 1
learning rate 4e-4 / 3e-4
cutoff len 4096
lora rank 16
lora alpha 16
lora dropout 0.05
lora target modules gate_proj, down_proj, up_proj
train on inputs False
add eos token False
group by length False
prompt template alpaca
lr scheduler cosine
warmup steps 100

I would reccomend using a batch size of 4-10, and cutt off length to ≤ 2048 to avoid using vram issues. Load_in_4bit, Normal Float, and bf16. For single 24 gig card.
If training with oobabooga you must edit the "training.py" file in the "oobabooga_windows\text-generation-webui\modules" folder. In line 49 edit standard modules to the modules you would like to target.
If training with alpaca lora use the argument --lora_target_modules when running the train.py command. To load in 4bit you must edit the train file, adding load in 4 bit, bf16, and normal float quant.

license:apache-2.0 region:us
{"license": "apache-2.0"}
TAGS
#license-apache-2.0 #region-us 

This is the dataset to the model used to train gorilla 7b but in the alpaca format, for lora training. 
Thank you to microsoft and uc berkly for open sourcing these datasets. As of now I do not believe this dataset works, will have to do more testing, but gorilla team plans to realease training code which might make it easer to see how this was fully done. and how it can be done with lora.

For ALPACA LORA users:
Modules you can target with lora:"gate_proj", "down_proj", "up_proj", "q_proj", "v_proj", "k_proj", "o_proj" 
Most lora models use:"q_proj", "v_proj", "k_proj", "o_proj" 
Platypus which got terrific results: "gate_proj", "down_proj", "up_proj"

Research on targeting certain modules still needs to be done, but if you don't want to train over a previously trained models newly learned abilities, target different modules than the ones used for original training.

Hyper perameters used by Platypus:
Hyperparameters for 13B and 70B Models
Hyperparameter Platypus2-13B / 70B
batch size 16
micro batch size 1
num epochs 1
learning rate 4e-4 / 3e-4
cutoff len 4096
lora rank 16
lora alpha 16
lora dropout 0.05
lora target modules gate_proj, down_proj, up_proj
train on inputs False
add eos token False
group by length False
prompt template alpaca
lr scheduler cosine
warmup steps 100

I would reccomend using a batch size of 4-10, and cutt off length to ≤ 2048 to avoid using vram issues. Load_in_4bit, Normal Float, and bf16. For single 24 gig card.
If training with oobabooga you must edit the "URL" file in the "oobabooga_windows\text-generation-webui\modules" folder. In line 49 edit standard modules to the modules you would like to target.
If training with alpaca lora use the argument --lora_target_modules when running the URL command. To load in 4bit you must edit the train file, adding load in 4 bit, bf16, and normal float quant.

TAGS
#license-apache-2.0 #region-us 

passage: TAGS
#license-apache-2.0 #region-us 
