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Model: unige-fti/Aladdin-3B
Source: Original Platform

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README.md

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+---
+library_name: transformers
+tags:
+- SmolLM-3B
+- Arabic
+language:
+- ar
+metrics:
+- chrf
+base_model:
+- HuggingFaceTB/SmolLM3-3B
+pipeline_tag: text-generation
+---
+
+# Model Card for unige-fti/Aladdin-3B
+
+Multidialectal Arabic generation and translation model fine-tuned for dialect fidelity and diglossia.
+
+## Model Details
+
+### Model Description
+
+- **Base model:** SmolLM3-3B
+- **Architecture:** Decoder-only causal transformer (SmolLM architecture)
+- **Parameters:** ~3B
+- **Language coverage:** Arabic dialects, Modern Standard Arabic (MSA), English
+
+Primary tasks:
+- Dialectal Arabic generation
+- Bidirectional translation (DA ↔ MSA ↔ English)
+- Controlled generation conditioned on dialect instructions
+
+This model was fine-tuned by the Aladdin-FTI team for the AMIYA shared task to jointly optimize:
+
+- Machine translation (semantic adequacy & diglossia)
+```
+Instruction-formatted prompts:
+
+Translate from English into Egyptian Arabic:
+<SOURCE>
+```
+- Instruction-conditioned generation (dialect fidelity)
+```
+Complete the sentence in Moroccan Arabic:
+<PREFIX>
+
+```
+
+The objective balances meaning preservation and dialect naturalness in Arabic diglossia settings.
+
+
+### Model Sources
+
+- **Repository:** [Github repository](https://github.com/drvenabili/mtfinetune_amiya/tree/main)
+- **Paper:** [https://arxiv.org/abs/2602.16290](https://arxiv.org/abs/2602.16290)
+
+
+## How to Get Started with the Model
+
+TODO
+
+## Training Details
+
+
+### Training Data: Closed-track training data only. 
+Datasets span multiple dialect regions and domains
+
+Parallel corpora:
+- SauDial
+- Casablanca corpus
+- JODA
+- UFAL Levantine
+- DODA
+- Atlas
+
+Monolingual dialect corpora:
+- MADAR
+- Shami
+- Saudi Tweets
+- EDGAD / EDC
+- HABIBI lyrics
+
+## Citation
+
+If you use this model in your research, please cite the following paper:
+
+```
+@inproceedings{mutal2026aladdinfti,
+  title     = {Aladdin-FTI @ AMIYA: Three Wishes for Arabic NLP: Fidelity, Diglossia, and Multidialectal Generation},
+  author    = {Mutal, Jonathan and Al Almaoui, Perla and Hengchen, Simon and Bouillon, Pierrette},
+  booktitle = {Proceedings of the AMIYA Shared Task, co-located with VarDial at EACL 2026},
+  year      = {2026},
+  address   = {Rabat, Morocco},
+  publisher = {Association for Computational Linguistics},
+}
+```
+
+## Compute infrastructure
+
+The computations were performed at the University of Geneva using the Baobab HPC service.

chat_template.jinja

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+{# ───── defaults ───── #}
+{%- if enable_thinking is not defined -%}
+{%- set enable_thinking = true -%}
+{%- endif -%}
+
+{# ───── reasoning mode ───── #}
+{%- if enable_thinking -%}
+  {%- set reasoning_mode = "/think" -%}
+{%- else -%}
+  {%- set reasoning_mode = "/no_think" -%}
+{%- endif -%}
+
+{# ───── header (system message) ───── #}
+{{- "<|im_start|>system\n" -}}
+
+{%- if messages[0].role == "system" -%}
+  {%- set system_message = messages[0].content -%}
+  {%- if "/no_think" in system_message -%}
+    {%- set reasoning_mode = "/no_think" -%}
+  {%- elif "/think" in system_message -%}
+    {%- set reasoning_mode = "/think" -%}
+  {%- endif -%}
+  {%- set custom_instructions = system_message.replace("/no_think", "").replace("/think", "").rstrip() -%}
+{%- endif -%}
+
+{%- if "/system_override" in system_message -%}
+  {{- custom_instructions.replace("/system_override", "").rstrip() -}}
+  {{- "<|im_end|>\n" -}}
+{%- else -%}
+  {{- "## Metadata\n\n" -}}
+  {{- "Knowledge Cutoff Date: June 2025\n" -}}
+  {%- set today = strftime_now("%d %B %Y") -%}
+  {{- "Today Date: " ~ today ~ "\n" -}}
+  {{- "Reasoning Mode: " + reasoning_mode + "\n\n" -}}
+  
+  {{- "## Custom Instructions\n\n" -}}
+  {%- if custom_instructions -%}
+    {{- custom_instructions + "\n\n" -}}
+  {%- elif reasoning_mode == "/think" -%}
+    {{- "You are a helpful AI assistant named SmolLM, trained by Hugging Face. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracking, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> Thought section </think> Solution section. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion.\n\n" -}}
+  {%- else -%}
+    {{- "You are a helpful AI assistant named SmolLM, trained by Hugging Face.\n\n" -}}
+  {%- endif -%}
+
+  {%- if xml_tools or python_tools or tools -%}
+    {{- "### Tools\n\n" -}}
+    {%- if xml_tools or tools -%}
+      {%- if tools -%}
+        {%- set xml_tools = tools -%}
+      {%- endif -%}
+      {%- set ns = namespace(xml_tool_string="You may call one or more functions to assist with the user query.\nYou are provided with function signatures within <tools></tools> XML tags:\n\n<tools>\n") -%}
+      {%- for tool in xml_tools[:] -%} {# The slicing makes sure that xml_tools is a list #}
+        {%- set ns.xml_tool_string = ns.xml_tool_string ~ (tool | string) ~ "\n" -%}
+      {%- endfor -%}
+      {%- set xml_tool_string = ns.xml_tool_string + "</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call>" -%}
+      {{- xml_tool_string -}}
+    {%- endif -%}
+    {%- if python_tools -%}
+      {%- set ns = namespace(python_tool_string="When you send a message containing Python code between '<code>' and '</code>' tags, it will be executed in a stateful Jupyter notebook environment, and you will then be given the output to continued reasoning in an agentic loop.\n\nYou can use the following tools in your python code like regular functions:\n<tools>\n") -%}
+      {%- for tool in python_tools[:] -%} {# The slicing makes sure that python_tools is a list #}
+        {%- set ns.python_tool_string = ns.python_tool_string ~ (tool | string) ~ "\n" -%}
+      {%- endfor -%}
+      {%- set python_tool_string = ns.python_tool_string + "</tools>\n\nThe state persists between code executions: so variables that you define in one step are still available thereafter." -%}
+      {{- python_tool_string -}}
+    {%- endif -%}
+    {{- "\n\n" -}}
+    {{- "<|im_end|>\n" -}}
+  {%- endif -%}
+{%- endif -%}
+{# ───── main loop ───── #}
+{%- for message in messages -%}
+    {%- set content = message.content if message.content is string else "" -%}
+    {%- if message.role == "user" -%}
+        {{ "<|im_start|>" + message.role + "\n"  + content + "<|im_end|>\n" }}
+    {%- elif message.role == "assistant" -%}
+        {% generation %}
+        {%- if reasoning_mode == "/think" -%}
+            {{ "<|im_start|>assistant\n" + content.lstrip("\n") + "<|im_end|>\n" }}
+        {%- else -%}
+            {{ "<|im_start|>assistant\n" + "<think>\n\n</think>\n" + content.lstrip("\n") + "<|im_end|>\n" }}
+        {%- endif -%}
+        {% endgeneration %}
+    {%- elif message.role == "tool" -%}
+    {{ "<|im_start|>" + "user\n"  + content + "<|im_end|>\n" }}
+    {%- endif -%}
+{%- endfor -%}
+{# ───── generation prompt ───── #}
+{%- if add_generation_prompt -%}
+    {%- if reasoning_mode == "/think" -%}
+        {{ "<|im_start|>assistant\n" }}
+    {%- else -%}
+        {{ "<|im_start|>assistant\n" + "<think>\n\n</think>\n"  }}
+    {%- endif -%}
+{%- endif -%}

config.json

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+{
+  "architectures": [
+    "SmolLM3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "float32",
+  "eos_token_id": 128012,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "layer_types": [
+    "full_attention",
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+    "full_attention",
+    "full_attention",
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+    "full_attention",
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+    "full_attention",
+    "full_attention",
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+    "full_attention",
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+    "full_attention",
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+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 65536,
+  "max_window_layers": 28,
+  "mlp_bias": false,
+  "model_type": "smollm3",
+  "no_rope_layer_interval": 4,
+  "no_rope_layers": [
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+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 4,
+  "pad_token_id": 128012,
+  "pretraining_tp": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 5000000.0,
+  "sliding_window": null,
+  "transformers_version": "4.57.3",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 128256
+}

generation_config.json

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+  "do_sample": true,
+  "eos_token_id": [
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+  ],
+  "pad_token_id": 128012,
+  "temperature": 0.6,
+  "top_p": 0.95,
+  "transformers_version": "4.57.3"
+}

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special_tokens_map.json

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+}

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