This model is a Saudi dialect conversational fine-tune of unsloth/Qwen3.5-4B, trained from the notebook qwen3-5-4b-saudi-dialect-sft-modal.ipynb and pushed to Hugging Face as a merged standalone model:

Model: https://huggingface.co/AyoubChLin/Qwen3.5-4B-saudi-dialect
LoRA adapters: https://huggingface.co/AyoubChLin/Qwen3.5-4B-saudi-dialect-lora
Dataset: https://huggingface.co/datasets/HeshamHaroon/saudi-dialect-conversations
Base model: https://huggingface.co/unsloth/Qwen3.5-4B

The training setup uses Unsloth + TRL SFTTrainer with LoRA adapters and then merges the adapters back into the base model for easier deployment.

Model Details

Base model: unsloth/Qwen3.5-4B
Fine-tuning method: LoRA SFT
Language: Arabic, focused on Saudi dialect conversations
Training modality in this run: text-only conversational SFT
Dataset split: 3545 total examples -> 3366 train / 179 eval
System prompt used in training: أنت مساعد مفيد يتحدث باللهجة السعودية العامية.
Tracking: Weights & Biases
W&B run: https://wandb.ai/cherguelainea/qwen-saudi-dialect/runs/6udmlaan

Training Arguments

Argument	Value
`max_seq_length`	`4096`
`load_in_4bit`	`False`
`load_in_8bit`	`False`
`lora_r`	`16`
`lora_alpha`	`16`
`lora_dropout`	`0`
`target_modules`	`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
`use_gradient_checkpointing`	`"unsloth"`
`per_device_train_batch_size`	`16`
`per_device_eval_batch_size`	`16`
`gradient_accumulation_steps`	`4`
Effective global batch size	`64`
`warmup_steps`	`5`
`num_train_epochs`	`4`
`learning_rate`	`4e-4`
`lr_scheduler_type`	`linear`
`optim`	`adamw_8bit`
`weight_decay`	`0.01`
`dataset_text_field`	`messages`
`packing`	`True` in config, but Unsloth reported `Sample packing skipped (vision-language model detected)`
`remove_unused_columns`	`False`
`save_strategy`	`steps`
`save_steps`	`100`
`eval_strategy`	`steps`
`eval_steps`	`50`
`seed`	`3407`
`report_to`	`wandb`
Precision used in this run	`bf16`

Training Results

Loss and Metrics

Metric	Value
`eval/loss`	`1.49976`
`train/loss` (final W&B summary)	`1.18529`
`training_loss` (`trainer_stats`)	`1.4871071903210766`
`train_runtime_seconds`	`2490.3044 s`
`train_runtime_minutes`	`41.51 min`
`train_samples_per_second`	`5.407`
`train_steps_per_second`	`0.085`
`eval/runtime`	`9.6061 s`
`eval/samples_per_second`	`18.53`
`eval/steps_per_second`	`1.249`
`train/global_step`	`212`
`train/epoch`	`4`
`train/grad_norm`	`0.69472`
`total_flos`	`7.760619536796672e+16`

Trainable Parameters

Item	Value
Total parameters	`4,560,499,200`
Trainable LoRA parameters	`21,233,664`
Trainable ratio	`0.4656%`

Hardware

Item	Value
GPU	`NVIDIA A100-SXM4-40GB`
Number of GPUs	`1`
CUDA toolkit	`12.9`
Torch	`2.8.0+cu129`
Transformers	`5.3.0`
Unsloth	`2026.3.6`
GPU total memory	`39.494 GB`
GPU memory reserved before training	`8.547 GB`
Peak reserved GPU memory	`38.455 GB`
Peak reserved GPU memory for LoRA training	`29.908 GB`
Peak GPU memory usage	`97.37%` of available GPU memory
System RAM	Not logged in the notebook outputs

Recorded memory numbers above are GPU memory / VRAM measurements taken from the training run. The notebook did not record host system RAM.

Data Preparation

The dataset examples are conversation turns stored under messages. During preprocessing, a Saudi Arabic system prompt is prepended to each conversation before fine-tuning. The training notebook keeps only valid conversations and then performs a 5% evaluation split with seed 3407.

Usage

Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "AyoubChLin/Qwen3.5-4B-saudi-dialect"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {"role": "system", "content": "أنت مساعد مفيد يتحدث باللهجة السعودية العامية."},
    {"role": "user", "content": "كيف حالك اليوم؟"},
]

input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    enable_thinking=False,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(
    input_ids,
    max_new_tokens=200,
    temperature=0.7,
    top_p=0.9,
)

print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))

Unsloth

Install

python

%%capture
import re, torch

v = re.match(r"[\d]{1,}\.[\d]{1,}", str(torch.__version__)).group(0)
xformers = "xformers==" + {
    "2.10": "0.0.34",
    "2.9": "0.0.33.post1",
    "2.8": "0.0.32.post2",
}.get(v, "0.0.34")

!pip install sentencepiece protobuf "datasets>=2.18.0" "huggingface_hub>=0.34.0" hf_transfer wandb
!pip install --no-deps unsloth_zoo bitsandbytes accelerate {xformers} peft trl triton unsloth
!pip install -q "transformers>=5.0.0"
!pip install -q --no-deps "trl>=0.15.0"

Run

python

from unsloth import FastLanguageModel

repo_id = "AyoubChLin/Qwen3.5-4B-saudi-dialect"
max_seq_length = 4096

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=repo_id,
    max_seq_length=max_seq_length,
    load_in_4bit=False,  # this repo was pushed as merged_16bit
)

FastLanguageModel.for_inference(model)

messages = [
    {
        "role": "system",
        "content": [
            {"type": "text", "text": "أنت مساعد مفيد يتحدث باللهجة السعودية العامية."}
        ],
    },
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "كيف حالك اليوم؟"}
        ],
    },
]

input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    enable_thinking=False,
    return_tensors="pt",
).to(model.device)

output_ids = model.generate(
    input_ids=input_ids,
    max_new_tokens=200,
    use_cache=True,
    temperature=0.7,
    top_p=0.9,
)

response = tokenizer.decode(
    output_ids[0][input_ids.shape[-1]:],
    skip_special_tokens=True,
)
print(response)

Notes

This repository contains the merged full model pushed with save_method="merged_16bit".
A separate LoRA adapter repository is also available: AyoubChLin/Qwen3.5-4B-saudi-dialect-lora.
The base checkpoint is multimodal-capable, but this fine-tune was trained on text-only dialogue data.
The training data is conversational and dialect-specific, so outputs may reflect biases or stylistic patterns present in the source dataset.