Model description

LLaMA is a family of open-source large language models from Meta AI that perform as well as closed-source models. This is the 13B parameter version, available for both inference and fine-tuning. Fine-tuning for this model is done with LoRA.

Note: LLaMA is for research purposes only. It is not intended for commercial use.

Fine-tuning

If you have access to the training beta , you can fine-tune this model.

Here’s an example using replicate-python :

training = replicate.trainings.create(
    version="replicate/llama-13b-lora:455d66312a66299fba685548fe24f66880f093007b927abd19f4356295f8577c", 
    input={
        "train_data": "https://storage.googleapis.com/dan-scratch-public/fine-tuning/70k_samples.jsonl", 
    destination="my-username/my-model"
Training takes these input parameters:
train_data (required): URL to a file where each row is a JSON record in the format {"prompt": ..., "completion": ...}. Can be JSONL or one JSON list.
train_batch_size (optional, default=1): Train batch size. For llama-13B, we recommend keeping the batch size small and increasing gradient_accumulation_steps
gradient_accumulation_steps (optional, default=8): Number of training steps (each of train_batch_size) to store gradients for before performing an optimizer step. 
learning_rate (optional, default=2e-5): Learning rate!
num_train_epochs (optional, default=1): Number of epochs (iterations over the entire training dataset) to train for. 
warmup_ratio (optional, default=0.03): Percentage of all training steps used for a linear LR warmup.
logging_steps (optional, default=1): Prints loss & other logging info every logging_steps. 
max_steps (optional, default=-1): Maximum number of training steps. Unlimited if max_steps=-1.
lora_rank (optional, default=8): Rank of the LoRA matrices. 
lora_alpha (optional, default=16): Alpha parameter for scaling LoRA weights; weights are scaled by alpha/rank
lora_dropout (optional, default=0.1): Dropout for LoRA training. 
lora_target_modules (optional, default=’q_proj,v_proj’): Comma-separated list of target modules to fine-tune with LoRA.
Citation
@misc{touvron2023llama,
      title={LLaMA: Open and Efficient Foundation Language Models}, 
      author={Hugo Touvron and Thibaut Lavril and Gautier Izacard and Xavier Martinet and Marie-Anne Lachaux and Timothée Lacroix and Baptiste Rozière and Naman Goyal and Eric Hambro and Faisal Azhar and Aurelien Rodriguez and Armand Joulin and Edouard Grave and Guillaume Lample},
      year={2023},
      eprint={2302.13971},
      archivePrefix={arXiv},
      primaryClass={cs.CL}