Bee1reason-arabic-Qwen-14B is a Large Language Model (LLM) fine-tuned from the unsloth/Qwen3-14B base model (which itself is based on Qwen/Qwen2-14B). This model has been specifically tailored to enhance logical and deductive reasoning capabilities in the Arabic language, while also maintaining its general conversational abilities. The fine-tuning process utilized LoRA (Low-Rank Adaptation) with the Unsloth library for high training efficiency. The LoRA weights were then merged with the base model to produce this standalone 16-bit (float16) precision model.

Key Features:

Built on unsloth/Qwen3-14B: Leverages the power and performance of the Qwen3 14-billion parameter base model.
Fine-tuned for Arabic Logical Reasoning: Trained on a dataset containing Arabic logical reasoning tasks.
Conversational Format: The model follows a conversational format, expecting user and assistant roles. It was trained on data that may include "thinking steps" (often within <think>...</think> tags) before providing the final answer, which is beneficial for tasks requiring explanation or complex inference.
Unsloth Efficiency: The Unsloth library was used for the fine-tuning process, enabling faster training and reduced GPU memory consumption.
Merged 16-bit Model: The final weights are a full float16 precision model, ready for direct use without needing to apply LoRA adapters to a separate base model.

Training Data

The model was primarily fine-tuned on a custom Arabic logical reasoning dataset, beetlware/arabic-reasoning-dataset-logic, available on the Hugging Face Hub. This dataset includes tasks variés types of reasoning (deduction, induction, abduction), with each task comprising the question text, a proposed answer, and a detailed solution including thinking steps.

This data was converted into a conversational format for training, typically with:

User Role: Containing the problem/question text.
Assistant Role: Containing the detailed solution, including thinking steps (often within <think>...</think> tags) followed by the final answer.

Fine-tuning Details

Base Model: unsloth/Qwen3-14B
Fine-tuning Technique: LoRA (Low-Rank Adaptation)
- r (rank): 32
- lora_alpha: 32
- target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
- lora_dropout: 0
- bias: "none"
Libraries Used: Unsloth (for efficient model loading and PEFT application) and Hugging Face TRL (SFTTrainer)
Max Sequence Length (max_seq_length): 2048 tokens
Training Parameters (example from notebook):
- per_device_train_batch_size: 2
- gradient_accumulation_steps: 4 (simulating a total batch size of 8)
- warmup_steps: 5
- max_steps: 30 (in the notebook, adjustable for a full run)
- learning_rate: 2e-4 (recommended to reduce to 2e-5 for longer training runs)
- optim: "adamw_8bit"
Final Save: LoRA weights were merged with the base model and saved in merged_16bit (float16) precision.

How to Use (with Transformers)

Since this is a merged 16-bit model, you can load and use it directly with the transformers library:

python

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import torch

model_id = "beetlware/Bee1reason-arabic-Qwen-14B"

# Load the Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Load the Model
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16, # or torch.float16 if bfloat16 is not supported
    device_map="auto", # Distributes the model on available devices (GPU/CPU)
)

# Ensure the model is in evaluation mode for inference
model.eval()

--- Example for Inference with Thinking Steps (if the model was trained to produce them) ---

Qwen3 models expect special tags for thinking ...

To enable thinking mode during inference (if supported by the fine-tuned model):

You might need to craft the prompt to ask the model to think.

Unsloth-trained Qwen3 models often respond to enable_thinking in tokenizer.apply_chat_template.

For a merged model, its ability to show depends on the training data.

python

user_prompt_with_thinking_request = "استخدم التفكير المنطقي خطوة بخطوة: إذا كان لدي 4 تفاحات والشجرة فيها 20 تفاحة، فكم تفاحة لدي إجمالاً؟" # "Use step-by-step logical thinking: If I have 4 apples and the tree has 20 apples, how many apples do I have in total?"

messages_with_thinking = [
    {"role": "user", "content": user_prompt_with_thinking_request}
]

# Apply the chat template
# Qwen3 uses a specific chat template. tokenizer.apply_chat_template is the correct way to format it.
chat_prompt_with_thinking = tokenizer.apply_chat_template(
    messages_with_thinking,
    tokenize=False,
    add_generation_prompt=True # Important for adding the assistant's generation prompt
)

inputs_with_thinking = tokenizer(chat_prompt_with_thinking, return_tensors="pt").to(model.device)

print("\n--- Inference with Thinking Request (Example) ---")
streamer_think = TextStreamer(tokenizer, skip_prompt=True)
with torch.no_grad(): # Important to disable gradients during inference
    outputs_think = model.generate(
        **inputs_with_thinking,
        max_new_tokens=512,
        temperature=0.6, # Recommended settings for reasoning by Qwen team
        top_p=0.95,
        top_k=20,
        pad_token_id=tokenizer.eos_token_id,
        streamer=streamer_think
    )

python

# --- Example for Normal Inference (Conversation without explicit thinking request) ---
user_prompt_normal = "ما هي عاصمة مصر؟" # "What is the capital of Egypt?"
messages_normal = [
    {"role": "user", "content": user_prompt_normal}
]

chat_prompt_normal = tokenizer.apply_chat_template(
    messages_normal,
    tokenize=False,
    add_generation_prompt=True
)
inputs_normal = tokenizer(chat_prompt_normal, return_tensors="pt").to(model.device)

print("\n\n--- Normal Inference (Example) ---")
streamer_normal = TextStreamer(tokenizer, skip_prompt=True)
with torch.no_grad():
    outputs_normal = model.generate(
        **inputs_normal,
        max_new_tokens=100,
        temperature=0.7, # Recommended settings for normal chat
        top_p=0.8,
        top_k=20,
        pad_token_id=tokenizer.eos_token_id,
        streamer=streamer_normal
    )

Usage with VLLM (for High-Throughput Scaled Inference)

VLLM is a library for fast LLM inference. Since you saved the model as merged_16bit, it can be used with VLLM.

Install VLLM:

bash

pip install vllm

(VLLM installation might have specific CUDA and PyTorch version requirements. Refer to the VLLM documentation for the latest installation prerequisites.)

Run the VLLM OpenAI-Compatible Server: You can serve the model using VLLM's OpenAI-compatible API server, making it easy to integrate into existing applications.

bash

python -m vllm.entrypoints.openai.api_server \
    --model beetlware/Bee1reason-arabic-Qwen-14B \
    --tokenizer beetlware/Bee1reason-arabic-Qwen-14B \
    --dtype bfloat16 \
    --max-model-len 2048 \
    # --tensor-parallel-size N  # If you have multiple GPUs
    # --gpu-memory-utilization 0.9 # To adjust GPU memory usage

Replace --dtype bfloat16 with float16 if needed.
max-model-len should match the max_seq_length you used.

Send Requests to the VLLM Server: Once the server is running (typically on http://localhost:8000), you can send requests using any OpenAI-compatible client, like the openai library:

python

import openai

client = openai.OpenAI(
    base_url="http://localhost:8000/v1", # VLLM server address
    api_key="dummy_key" # VLLM doesn't require an actual API key by default
)

completion = client.chat.completions.create(
    model="beetlware/Bee1reason-arabic-Qwen-14B", # Model name as specified in VLLM
    messages=[
        {"role": "user", "content": "اشرح نظرية النسبية العامة بكلمات بسيطة."} # "Explain the theory of general relativity in simple terms."
    ],
    max_tokens=256,
    temperature=0.7,
    stream=True # To enable streaming
)

print("Streaming response from VLLM:")
full_response = ""
for chunk in completion:
    if chunk.choices[0].delta.content is not None:
        token = chunk.choices[0].delta.content
        print(token, end="", flush=True)
        full_response += token
print("\n--- End of stream ---")

Limitations and Potential Biases

The model's performance is highly dependent on the quality and diversity of the training data. It may exhibit biases present in the data it was trained on. Despite fine-tuning for logical reasoning, the model might still make errors on very complex or unfamiliar reasoning tasks. The model may "hallucinate" or produce incorrect information, especially for topics not well-covered in its training data. Capabilities in languages other than Arabic (if primarily trained on Arabic) might be limited.

Additional Information

Developed by: [loai abdalslam/Organization - beetleware] Upload/Release Date: [21-5-2025] Contact / Issue Reporting: [loai.abdalsalm@beetleware.com]

Beetleware :

We are a software house and digital transformation service provider that was founded six years ago and is based in Saudi Arabia.

Our Offices

KSA Office (+966) 54 597 3282 ahmed.taha@beetleware.com

Egypt Office (+2) 010 67 256 306 ahmed.abullah@beetleware.com

Oman Office (+968) 9522 8632

Uploaded model

Developed by: beetlware AI Team
License: apache-2.0
Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit

This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.