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atharvashembade/
Llama-3.2-1B_LoRA

Name: Llama-3.2-1B_LoRA
Availability: InStock

safetensors

meta-llama/Llama-3.2-1B

finetune

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🦙 LoRA Implementation of LLaMA

This model is a Low-Rank Adaptation (LoRA) of the LLaMA 3.2-1B model.

Original size: ~1 Billion parameters
LoRA fine-tuned parameters: ~86,000
Result: Achieves similar performance on many prompts with dramatically reduced trainable weights.

About

This model was fine-tuned using the PEFT (Parameter-Efficient Fine-Tuning) library and Hugging Face's transformers.

Base Model: meta-llama/Llama-3.2-1B

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("your-username/Llama-3.2-1B_LoRA")
tokenizer = AutoTokenizer.from_pretrained("your-username/Llama-3.2-1B_LoRA")

inputs = tokenizer("Tell me something interesting about space.", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

atharvashembade/Llama-3.2-1B_LoRA

🦙 LoRA Implementation of LLaMA

About

Usage

atharvashembade/
Llama-3.2-1B_LoRA