metadata

language:
  - bn
license: apache-2.0
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - llama
  - trl
base_model: unsloth/llama-3-8b-bnb-4bit
inference: false

LLama-3 Bangla 4 bit

Developed by: KillerShoaib
License: apache-2.0
Finetuned from model : unsloth/llama-3-8b-bnb-4bit
Datset used for fine-tuning : iamshnoo/alpaca-cleaned-bengali

4-bit Quantization

This is 4-bit quantization of Llama-3 8b model.

Llama-3 Bangla Different Formats

LoRA Adapters only - KillerShoaib/llama-3-8b-bangla-lora
GGUF q4_k_m - KillerShoaib/llama-3-8b-bangla-GGUF-Q4_K_M

Model Details

Llama 3 8 billion model was finetuned using unsloth package on a cleaned Bangla alpaca dataset. After that the model was quantized in 4-bit. The model is finetuned for 2 epoch on a single T4 GPU.

Pros & Cons of the Model

Pros

The model can comprehend the Bangla language, including its semantic nuances
Given context model can answer the question based on the context

Cons

Model is unable to do creative or complex work. i.e: creating a poem or solving a math problem in Bangla
Since the size of the dataset was small, the model lacks lot of general knowledge in Bangla

Run The Model

FastLanguageModel from unsloth for 2x faster inference


from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "KillerShoaib/llama-3-8b-bangla-4bit",
    max_seq_length = 2048,
    dtype = None,
    load_in_4bit = True,
)
FastLanguageModel.for_inference(model)

# alpaca_prompt for the model
alpaca_prompt = """Below is an instruction in bangla that describes a task, paired with an input also in bangla that provides further context. Write a response in bangla that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

# input with instruction and input
inputs = tokenizer(
[
    alpaca_prompt.format(
        "সুস্থ থাকার তিনটি উপায় বলুন", # instruction
        "", # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

# generating the output and decoding it
outputs = model.generate(**inputs, max_new_tokens = 2048, use_cache = True)
tokenizer.batch_decode(outputs)

AutoModelForCausalLM from Hugginface

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "KillerShoaib/llama-3-8b-bangla-4bit"  # YOUR MODEL YOU USED FOR TRAINING either hf hub name or local folder name.
tokenizer_name = model_name

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
# Load model
model = AutoModelForCausalLM.from_pretrained(model_name)

alpaca_prompt = """Below is an instruction in bangla that describes a task, paired with an input also in bangla that provides further context. Write a response in bangla that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

inputs = tokenizer(
[
    alpaca_prompt.format(
        "সুস্থ থাকার তিনটি উপায় বলুন", # instruction
        "", # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens = 1024, use_cache = True)
tokenizer.batch_decode(outputs)

Inference Script & Github Repo

Google Colab - Llama-3 8b Bangla Inference Script
Github Repo - Llama-3 Bangla