Instructions to use asadullahusama17/tinyllama-lora-unsloth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use asadullahusama17/tinyllama-lora-unsloth with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/tinyllama-bnb-4bit") model = PeftModel.from_pretrained(base_model, "asadullahusama17/tinyllama-lora-unsloth") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
TinyLlama LoRA Fine-tune (Unsloth)
LoRA adapter fine-tuned on top of unsloth/tinyllama-bnb-4bit using Unsloth.
Training Configuration
- LoRA rank (r): 32
- LoRA alpha: 32
- LoRA dropout: 0.0
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- Max sequence length: 4096
- Quantization: 4-bit
- Seed: 3407
Training Code
- Full training notebook and pipeline: https://github.com/MalikUsama9/tinyllama-lora-unsloth
- Downloads last month
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Model tree for asadullahusama17/tinyllama-lora-unsloth
Base model
unsloth/tinyllama-bnb-4bit