artificialguybr/llama3-8b-alpacadata-ptbr

See axolotl config

axolotl version: 0.4.0

base_model: NousResearch/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: dominguesm/alpaca-data-pt-br
    type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./out-llama8b-alpaca-data-pt-br

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

wandb_project: meta-llama-8b-alpacadata-br
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

LLama 3- 8B -alpaca-data-pt-br

Thanks to Redmond.ai for the GPU Support!

This model is a fine-tuned version of NousResearch/Meta-Llama-3-8B on the dominguesm/alpaca-data-pt-br dataset. It achieves the following results on the evaluation set:

• Loss: 1.1227

Model description

The model is a Portuguese language understanding model designed to generate responses to a wide range of questions and prompts. It takes as input a natural language question or prompt and outputs a corresponding response.

The model is trained on a dataset of 51k examples, which is a cleaned and translated version of the original Alpaca Dataset released by Stanford. The original dataset was translated to Portuguese (Brazil) to provide a more culturally and linguistically relevant resource for the Brazilian market.

The dataset was carefully reviewed to identify and fix issues present in the original release, ensuring that the model is trained on high-quality data. The model is intended to be used in applications where a deep understanding of Portuguese language is required, such as chatbots, virtual assistants, and language translation systems.

Intended uses:

Generating responses to natural language questions and prompts in Portuguese

Supporting chatbots, virtual assistants, and other conversational AI applications

Enhancing language translation systems and machine translation models

Providing a culturally and linguistically relevant resource for the Brazilian market

Limitations

The model may not generalize well to other languages or dialects

The model may not perform well on out-of-domain or unseen topics

The model may not be able to handle ambiguous or open-ended prompts

The model may not be able to understand nuances of regional dialects or slang

The model may not be able to handle prompts that require common sense or real-world knowledge

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
1.382	0.01	1	1.4056
1.1762	0.5	45	1.1987
1.1294	0.99	90	1.1493
1.0028	1.47	135	1.1331
0.9899	1.97	180	1.1227

Framework versions

• Transformers 4.40.0.dev0
• Pytorch 2.2.2+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0