Instructions to use Omartificial-Intelligence-Space/al-baka-16bit-llama3-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Omartificial-Intelligence-Space/al-baka-16bit-llama3-8b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Omartificial-Intelligence-Space/al-baka-16bit-llama3-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Omartificial-Intelligence-Space/al-baka-16bit-llama3-8b")
model = AutoModelForCausalLM.from_pretrained("Omartificial-Intelligence-Space/al-baka-16bit-llama3-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Omartificial-Intelligence-Space/al-baka-16bit-llama3-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Omartificial-Intelligence-Space/al-baka-16bit-llama3-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Omartificial-Intelligence-Space/al-baka-16bit-llama3-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Omartificial-Intelligence-Space/al-baka-16bit-llama3-8b

SGLang

How to use Omartificial-Intelligence-Space/al-baka-16bit-llama3-8b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Omartificial-Intelligence-Space/al-baka-16bit-llama3-8b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Omartificial-Intelligence-Space/al-baka-16bit-llama3-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Omartificial-Intelligence-Space/al-baka-16bit-llama3-8b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Omartificial-Intelligence-Space/al-baka-16bit-llama3-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Omartificial-Intelligence-Space/al-baka-16bit-llama3-8b with Docker Model Runner:
```
docker model run hf.co/Omartificial-Intelligence-Space/al-baka-16bit-llama3-8b
```

🚀 al-baka-llama3-8b (Main Model)

Al Baka is an Fine Tuned Model based on the new released LLAMA3-8B Model on the Stanford Alpaca dataset Arabic version Yasbok/Alpaca_arabic_instruct.

Model Summary

Model Type: Llama3-8B FineTuned Model (16-bit Version)
Language(s): Arabic, English
Base Model: LLAMA-3-8B
Dataset: Yasbok/Alpaca_arabic_instruct

Model Details

The model was fine-tuned and mergen in 16-bit precision using unsloth

How to Get Started with the Model

Setup

# Install packages
%%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

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

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 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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Safetensors

Model size

8B params

Tensor type

BF16

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Collection including Omartificial-Intelligence-Space/al-baka-16bit-llama3-8b

Arabic LLAMA3 & 3.1 FineTuned Models

Collection

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