Instructions to use NeuralVulture/llama32-1b-qa-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use NeuralVulture/llama32-1b-qa-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.2-1B-Instruct") model = PeftModel.from_pretrained(base_model, "NeuralVulture/llama32-1b-qa-lora") - Notebooks
- Google Colab
- Kaggle
File size: 1,001 Bytes
07196d7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | {
"references": [
"https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide",
"https://huggingface.co/blog/ImranzamanML/fine-tuning-1b-llama-32-a-comprehensive-article"
],
"model_id": "unsloth/Llama-3.2-1B-Instruct",
"model_source": "unsloth/Llama-3.2-1B-Instruct",
"data_path": "/data/training/qa_sft_messages.jsonl",
"hf_dataset": "NeuralVulture/ai-concepts-qa",
"hf_split": "train",
"hf_config": null,
"lora_r": 16,
"lora_alpha": 32,
"lora_dropout": 0.05,
"target_modules": [
"q_proj",
"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"up_proj",
"down_proj"
],
"learning_rate": 0.0002,
"lr_scheduler_type": "cosine",
"warmup_steps": 0.05,
"weight_decay": 0.01,
"num_train_epochs": 2.0,
"per_device_train_batch_size": 2,
"gradient_accumulation_steps": 8,
"effective_batch_size": 16,
"max_length": 4096,
"assistant_only_loss": false,
"train_rows": 380,
"eval_rows": 20,
"data_file": null
} |