Instructions to use Mubin1917/lamini_docs_LoRA_adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mubin1917/lamini_docs_LoRA_adapter with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Mubin1917/lamini_docs_LoRA_adapter", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Mubin1917/lamini_docs_LoRA_adapter 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 Mubin1917/lamini_docs_LoRA_adapter 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 Mubin1917/lamini_docs_LoRA_adapter to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Mubin1917/lamini_docs_LoRA_adapter to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Mubin1917/lamini_docs_LoRA_adapter", max_seq_length=2048, )
Uploaded Model: LORA Adapter
- Developed by: Mubin1917
- License: apache-2.0
- Finetuned from model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
This LORA adapter is based on the unsloth/meta-llama-3.1-8b-instruct-bnb-4bit model and has been fine-tuned on the Lamini_docs QnA dataset. The fine-tuning process was optimized using Unsloth and Huggingface's TRL library, resulting in a 2x faster training time.
Training Configuration
The model was trained with the following configuration:
training_args = TrainingArguments(
num_train_epochs=6,
per_device_train_batch_size=4,
gradient_accumulation_steps=4,
per_device_eval_batch_size=4,
eval_accumulation_steps=4,
warmup_steps=50,
learning_rate=2e-4,
fp16=not torch.cuda.is_bf16_supported(),
bf16=torch.cuda.is_bf16_supported(),
eval_steps=25, # Evaluate every 25 steps
logging_steps=25,
optim="adamw_8bit",
weight_decay=0.01,
lr_scheduler_type="linear",
seed=3407,
output_dir="/kaggle/temp/results",
report_to="wandb",
save_total_limit=1, # Save the best one and the last one
metric_for_best_model="val_loss",
eval_strategy="steps",
load_best_model_at_end=True,
)
Evaluation Results
SacreBLEU Test:
Score: 73.55
Detailed Metrics:- Counts: [20894, 19191, 18504, 18029]
- Totals: [26214, 26074, 25934, 25794]
- Precisions: [79.71%, 73.60%, 71.35%, 69.90%]
- Brevity Penalty: 1.0
- System Length: 26214
- Reference Length: 24955
BLEU Test:
BLEU Score: 0.767
Detailed Metrics:- Precisions: [79.71%, 73.73%]
- Brevity Penalty: 1.0
- Length Ratio: 1.05
- Translation Length: 26299
- Reference Length: 24955
For a detailed comparison between the predicted and actual QnA responses on the test dataset, please visit the evaluation dataset.
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