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--- |
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license: apache-2.0 |
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language: |
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- ar |
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tags: |
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- alpaca |
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- llama3 |
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- arabic |
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--- |
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# 🚀 al-baka-llama3-8b |
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[<img src="https://i.ibb.co/fMsBM0M/Screenshot-2024-04-20-at-3-04-34-AM.png" width="150"/>](https://www.omarai.co) |
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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). |
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## Model Summary |
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- **Model Type:** Llama3-8B FineTuned Model |
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- **Language(s):** Arabic |
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- **Base Model:** [LLAMA-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) |
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- **Dataset:** [Yasbok/Alpaca_arabic_instruct](https://huggingface.co/datasets/Yasbok/Alpaca_arabic_instruct) |
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## Model Details |
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- The model was fine-tuned in 4-bit precision using [unsloth](https://github.com/unslothai/unsloth) |
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- The run is performed only for 1000 steps with a single Google Colab T4 GPU NVIDIA GPU with 15 GB of available memory. |
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<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> |
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## How to Get Started with the Model |
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### Setup |
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```python |
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# Install packages |
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%%capture |
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import torch |
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major_version, minor_version = torch.cuda.get_device_capability() |
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!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git" |
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if major_version >= 8: |
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# Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40) |
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!pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes |
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else: |
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# Use this for older GPUs (V100, Tesla T4, RTX 20xx) |
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!pip install --no-deps xformers trl peft accelerate bitsandbytes |
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pass |
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``` |
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### First, Load the Model |
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```python |
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from unsloth import FastLanguageModel |
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import torch |
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max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally! |
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dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+ |
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load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False. |
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model, tokenizer = FastLanguageModel.from_pretrained( |
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model_name = "Omartificial-Intelligence-Space/al-baka-16bit-llama3-8b", |
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max_seq_length = max_seq_length, |
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dtype = dtype, |
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load_in_4bit = load_in_4bit, |
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# token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf |
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) |
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``` |
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### Second, Try the model |
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```python |
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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. |
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### Instruction: |
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{} |
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### Input: |
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{} |
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### Response: |
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{}""" |
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# alpaca_prompt = Copied from above |
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FastLanguageModel.for_inference(model) # Enable native 2x faster inference |
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inputs = tokenizer( |
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[ |
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alpaca_prompt.format( |
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"استخدم البيانات المعطاة لحساب الوسيط.", # instruction |
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"[2 ، 3 ، 7 ، 8 ، 10]", # input |
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"", # output - leave this blank for generation! |
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) |
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], return_tensors = "pt").to("cuda") |
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outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True) |
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tokenizer.batch_decode(outputs) |
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``` |
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### Recommendations |
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- [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. |
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