Instructions to use alexiaassis/Modelo-Treinado-Mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use alexiaassis/Modelo-Treinado-Mistral with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-instruct-v0.3-bnb-4bit") model = PeftModel.from_pretrained(base_model, "alexiaassis/Modelo-Treinado-Mistral") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use alexiaassis/Modelo-Treinado-Mistral with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for alexiaassis/Modelo-Treinado-Mistral to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for alexiaassis/Modelo-Treinado-Mistral to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for alexiaassis/Modelo-Treinado-Mistral to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="alexiaassis/Modelo-Treinado-Mistral", max_seq_length=2048, )
File size: 844 Bytes
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dataset: treino_pt_rde3
dataset_dir: data
ddp_timeout: 180000000
do_train: true
double_quantization: true
enable_thinking: true
finetuning_type: lora
flash_attn: auto
fp16: true
gradient_accumulation_steps: 2
include_num_input_tokens_seen: true
learning_rate: 3.0e-05
logging_steps: 10
lora_alpha: 16
lora_dropout: 0
lora_rank: 8
lora_target: all
lr_scheduler_type: cosine
max_grad_norm: 1.0
max_samples: 3716
model_name_or_path: mistralai/Mistral-7B-Instruct-v0.3
num_train_epochs: 3.0
optim: adamw_torch
output_dir: saves/Mistral-7B-Instruct-v0.3/lora/mistral-treinado
packing: false
per_device_train_batch_size: 4
plot_loss: true
preprocessing_num_workers: 16
quantization_bit: 4
quantization_method: bnb
report_to: none
save_steps: 1000
stage: sft
template: alpaca
trust_remote_code: true
use_unsloth: true
warmup_steps: 0
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