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Aananda-giri/
LLAMA3-Nepali

Name: LLAMA3-Nepali
Availability: InStock

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LLAMA3.2 Nepali 318M Model

Overview

This is a 318M parameter LLAMA3.2 model fine-tuned on a Nepali text dataset. The model is designed for generating coherent and contextually relevant Nepali text.

Resources

Training Code: GitHub Repository
Chat Interface: Hugging Face Space
Dataset: IRIISNEPAL/Nepali-Text-Corpus and nepberta
Reference Book: Build a Large Language Model (From Scratch) by Sebastian Raschka, PhD

Installation

To install the required dependencies, run:

pip install datasets huggingface_hub matplotlib transformers torch --quiet

Usage

1. Download Model Weights

python

from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="Aananda-giri/LLAMA3-Nepali", filename="parameters_300m/model_pg_398000_steps.pth", local_dir="./")

2. Load the Tokenizer

python

from transformers import PreTrainedTokenizerFast

tokenizer = PreTrainedTokenizerFast.from_pretrained("Aananda-giri/LLAMA3-Nepali")
tokenizer.save_pretrained("NepaliBPE")

3. Download Additional Scripts

python

import requests
res=requests.get(r"https://raw.githubusercontent.com/Aananda-giri/LLAMA3-Nepali/main/4.%20inference/2_inference/previous_chapters.py")
with open('previous_chapters.py', 'w') as f:
    f.write(res.text)

4. Load the Model

python

import torch
from previous_chapters import Llama3Model, ChatFormat, Tokenizer, generate_and_print_sample

# Initialize tokenizer
_tokenizer = Tokenizer("NepaliBPE/tokenizer.json")
chat_tokenizer = ChatFormat(_tokenizer)

# Define model configuration
LLAMA32_CONFIG = {
    "vocab_size": 50006,
    "context_length": 512,
    "emb_dim": 1320,
    "n_heads": 20,
    "n_layers": 10,
    "hidden_dim": 5280,
    "n_kv_groups": 5,
    "rope_base": 500_000.0,
    "dtype": torch.bfloat16,
    "rope_freq": {
        "factor": 32.0,
        "low_freq_factor": 1.0,
        "high_freq_factor": 4.0,
        "original_context_length": 8192,
    }
}

# Adjust RoPE Scaling
old_context_length = 131_072
new_context_length = LLAMA32_CONFIG["context_length"]
LLAMA32_CONFIG["rope_base"] *= new_context_length / old_context_length

# Load Model
model = Llama3Model(LLAMA32_CONFIG)
model.eval()

# Optimize model if PyTorch 2.0 is available
if torch.__version__ >= "2.0":
    model = torch.compile(model)

5. Load Model Weights

python

# Move model to device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
print(f'device: {device}')

# Load checkpoint
latest_model_checkpoint = "parameters_300m/model_pg_398000_steps.pth"
checkpoint = torch.load(latest_model_checkpoint, map_location=device, weights_only=False)
model.load_state_dict(checkpoint["model_state_dict"])

6. Generate Text

python

# Generate text sample
generate_and_print_sample(
    PROMPT="रामले भात",
    tokenizer=_tokenizer,
    chat_tokenizer=chat_tokenizer,
    model=model,
    device=device,
    context_length=LLAMA32_CONFIG["context_length"]
)

Advanced Text Generation

python

from previous_chapters import generate_chat_optimized
import time

start_time = time.time()
output_text = generate_chat_optimized(
    prompt="रामले भात",
    tokenizer=tokenizer,
    chat_tokenizer=chat_tokenizer,
    model=model,
    max_new_tokens=20,
    context_size=512,
    device=device,
    temperature=0.3,
    top_k=5,
    top_p=None,
    eos_id=None,
    repetition_penalty=1.2,
    penalize_len_below=10,
    batch_size=1  # Added parameter
)

print(f"time:{time.time() - start_time}\n output_text: {output_text}")

Model Checkpoints

The best-performing checkpoint is parameters_300m/model_pg_398000_steps.pth. Additionally, other folders contain experimental checkpoints from various training runs.