from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "shinigamiRaj/Vedas-Llama-3.2-3B-LoRA",
    load_in_4bit = True,
)
FastLanguageModel.for_inference(model)

2. Using the Merged 16-bit Model

Repo: shinigamiRaj/Vedas-Llama-3.2-3B-Merged

python

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("shinigamiRaj/Vedas-Llama-3.2-3B-Merged")
tokenizer = AutoTokenizer.from_pretrained("shinigamiRaj/Vedas-Llama-3.2-3B-Merged")

3. Using GGUF (LM Studio / Ollama)

Repo: shinigamiRaj/Vedas-Llama-3.2-3B-GGUF Download the .gguf file and load it into your preferred local LLM runner.

📜 Training Configuration

Epochs: 1
Learning Rate: 2e-4
Optimizer: AdamW 8-bit
Hardware: Tesla T4 (Google Colab)

⚠️ Disclaimer

This model is designed for educational exploration of Vedic texts. Verify outputs against primary sources.