AyoubChLin
/

LFM2.5-1.2B-Instruct-Saudi-Dialect-1

@@ -6,197 +6,219 @@ base_model:
 - LiquidAI/LFM2.5-1.2B-Instruct
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
 ### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
 ## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 - LiquidAI/LFM2.5-1.2B-Instruct
 ---
+# 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.
+---
+## 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
+---
+## Training Details
+### Base Model
+* Architecture: Hybrid state-space + attention
+* Parameters: ~1.17B
+* Context length: 32,768 tokens
+* Training tokens: ~28T
+* Languages: Multilingual including Arabic
+---
+### 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.
+---
+### Training Configuration
+(Extracted from training notebook)
+| Parameter             | Value                        |
+| --------------------- | ---------------------------- |
+| Epochs                | 8                            |
+| Learning Rate         | 2e-4                         |
+| Batch Size            | 16                            |
+| Gradient Accumulation | 4                            |
+| Optimizer             | AdamW                        |
+| LR Scheduler          | Linear                       |
+| Warmup Ratio          | 0.03                         |
+| Sequence Length       | 8096                         |
+| Precision             | FP16                         |
+| Training Type         | Supervised Fine-Tuning (SFT) |
+---
 ### 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.
+---
 ## 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.
+---
+## 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
+---
+## 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
+---
+## Hardware Requirements
+Runs efficiently on:
+* CPU inference (<1GB memory quantized)
+* Mobile NPUs
+* Edge devices
+---
+## Example Usage
+```python
+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))
+```
+---
+## Training Compute
+* **GPU:** 1 × NVIDIA A100 (40 GB VRAM)
+* **CPU:** 8 cores
+* **RAM:** 16 GiB
+* **Compute Environment:** Cloud training instance
+---
+## License
+Same as base model license unless otherwise specified.
+---
+## Citation
+If you use this model:
+```
+@misc{saudi-dialect-lfm2.5,
+  author = {Cherguelaine Ayoub},
+  title = {Saudi Dialect LFM2.5},
+  year = {2026},
+  publisher = {Hugging Face}
+}
+```
+---
+## Acknowledgments
+* Liquid AI for base model
+* Dataset creators
+* Open-source tooling ecosystem
+---