Model Card for FalconAlpaca
FalconAlpaca is Falcon-7B trained on the Stanford Alpaca Dataset
Model Details
This model was an attempt to influence the learned outputs of Falcon-7B to adapt the outputs to become more information-rich and focused. Trained using Lit GPT, the model took 2 hours to train on 1 4xA6000 node.
Model Description
- License: [Apache 2.0]
- Finetuned from model : Falcon-7B
Model Sources
Out-of-Scope Use
This model is not intended for anything but testing purposes. There have been no attempts to control/remove bias, toxicity, or any other form of potentially dangerous or harmful messages.
Bias, Risks, and Limitations
No effort was made to remove any wrong or harmful information from Falcon-7B or the Alpaca dataset. Any risks and limitations from either of those datasets/models carry over to this project as well.
How to Get Started with the Model
Download and install libraries for Lit GPT
python generate/adapter_v2.py \
--adapter_path path/to/model/lit_model_adapter_finetuned.pth \
--checkpoint_dir path/to/model \
--prompt "What temperature should I cook pork at to ensure it is safe?"
This uses around 14GB of VRAM. If you need to use less VRAM, you can add the parameters
--quantize llm.int8
or
--quantize gptq.int4
Training Data
Training Hyperparameters
The defaults were as follows
learning_rate = 9e-3
batch_size = 32
micro_batch_size = 2
gradient_accumulation_iters = 16
epoch_size = 50000
num_epochs = 5
max_iters = 125000
weight_decay = 0.02
warmup_iters = 50000
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