AyoubChLin / lfm2.5-saudi-dialect

Saudi Dialect LFM2.5 — Instruction-Tuned Arabic Dialect Model

Model Description

This model is a fine-tuned version of Liquid AI ’s LFM2.5‑1.2B‑Instruct , adapted for Saudi dialect conversational generation.

The base model belongs to the LFM2.5 family — hybrid state-space + attention language models designed for fast on-device inference ,low memory usage, and strong performance relative to size. It contains ~1.17B parameters, 32k context length, and supports multilingual generation including Arabic.

This fine-tuned variant specializes the model for Saudi dialect conversational patterns , improving fluency, dialect authenticity, and instruction following for regional Arabic use cases.

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Intended Use

Primary Use Cases

• Saudi dialect chatbots
• Customer support assistants
• Conversational agents
• Arabic NLP research
• Dialect-aware RAG pipelines
• Dialogue generation systems

Out-of-Scope Uses

• Legal/medical advice
• Safety-critical decision making
• High-precision knowledge tasks without retrieval
• Sensitive content generation

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Training Details

Base Model

• Architecture: Hybrid state-space + attention
• Parameters: ~1.17B
• Context length: 32,768 tokens
• Training tokens: ~28T
• Languages: Multilingual including Arabic

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Dataset

Fine-tuned on:

Dataset: HeshamHaroon/saudi-dialect-conversations

Domain: Conversational dialogue

Language: Saudi dialect Arabic

Format: Instruction → Response pairs

Purpose: Increase dialect authenticity and conversational naturalness.

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Training Configuration

(Extracted from training notebook)

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Training Procedure

Training was performed using:

• Transformers
• TRL SFTTrainer
• LoRA fine-tuning
• Mixed precision
• Gradient accumulation

The base model weights were adapted rather than retrained from scratch.

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Evaluation

Qualitative evaluation indicates:

• Improved dialect fluency
• Reduced MSA leakage
• Better conversational tone
• Higher lexical authenticity

Dialect-specific fine-tuning is known to significantly increase dialect generation accuracy and reduce standard-Arabic drift in Arabic LLMs.

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Performance Characteristics

Strengths

• Very fast inference
• Low memory footprint
• Strong conversational coherence
• Good instruction following

Limitations

• Smaller model → limited factual depth
• May hallucinate
• Less capable for complex reasoning vs larger models
• Dialect bias toward Saudi Arabic

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Bias, Risks, and Safety

Potential risks:

• Dialect bias
• Cultural bias from dataset
• Toxic outputs if prompted maliciously
• Hallucinated facts

Mitigations:

• Filtering dataset
• Instruction alignment
• Moderation layers recommended

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Hardware Requirements

Runs efficiently on:

• CPU inference (<1GB memory quantized)
• Mobile NPUs
• Edge devices

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Example Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "AyoubChLin/lfm2.5-saudi-dialect"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "تكلم باللهجة السعودية عن القهوة"

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Training Compute

• GPU: 1 × NVIDIA A100 (40 GB VRAM)
• CPU: 8 cores
• RAM: 16 GiB
• Compute Environment: Cloud training instance

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License

Same as base model license unless otherwise specified.

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Citation

If you use this model:

@misc{saudi-dialect-lfm2.5,
  author = {Cherguelaine Ayoub},
  title = {Saudi Dialect LFM2.5},
  year = {2026},
  publisher = {Hugging Face}
}

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Acknowledgments

• Liquid AI for base model
• Dataset creators
• Open-source tooling ecosystem

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