---
license: apache-2.0
language:
- ar
tags:
- alpaca
- llama3
- arabic
---

# 🚀 al-baka-llama3-8b
Al Baka is an Experimental Fine Tuned Model based on the new released LLAMA3-8B Model on the Stanford Alpaca dataset Arabic version [Yasbok/Alpaca_arabic_instruct](https://huggingface.co/datasets/Yasbok/Alpaca_arabic_instruct).

## Model Summary

- **Model Type:** Llama3-8B FineTuned Model
- **Language(s):** Arabic
- **Base Model:** [LLAMA-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
- **Dataset:** [Yasbok/Alpaca_arabic_instruct](https://huggingface.co/datasets/Yasbok/Alpaca_arabic_instruct)

## Model Details

- The model was fine-tuned in 4-bit precision using [unsloth](https://github.com/unslothai/unsloth)

- The run is performed only for 1000 steps with a single Google Colab T4 GPU NVIDIA GPU with 15 GB of available memory.


<span style="color:red">The model is currently being Experimentally Fine Tuned to assess LLaMA-3's response to Arabic, following a brief period of fine-tuning. Larger and more sophisticated models will be introduced soon.</span>

## How to Get Started with the Model

### Setup
```python
# Install packages
!pip install accelerate bitsandbytes
%%capture
import torch
major_version, minor_version = torch.cuda.get_device_capability()
!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
if major_version >= 8:
    # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)
    !pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes
else:
    # Use this for older GPUs (V100, Tesla T4, RTX 20xx)
    !pip install --no-deps xformers trl peft accelerate bitsandbytes
pass
```

### First, Load the Model
```python
from unsloth import FastLanguageModel
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.


model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "Omartificial-Intelligence-Space/al-baka-16bit-llama3-8b",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)
```

### Second, Try the model 
```python
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

# alpaca_prompt = Copied from above
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
inputs = tokenizer(
[
    alpaca_prompt.format(
       "استخدم البيانات المعطاة لحساب الوسيط.", # instruction
        "[2 ، 3 ، 7 ، 8 ، 10]", # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
tokenizer.batch_decode(outputs)
```

### Recommendations

- [unsloth](https://github.com/unslothai/unsloth) for finetuning models. You can get a 2x faster finetuned model which can be exported to any format or uploaded to Hugging Face.