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Model: cs-552-2026-Flash-McQueenS-and-TheKing/multilingual_model
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2026-06-21 15:56:17 +08:00
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---
library_name: transformers
tags: []
---
# 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:** Myriam Sarah Achour
- **Funded by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## 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
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
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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.
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
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#### Metrics
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### Results
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#### Summary
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<!-- Relevant interpretability work for the model goes here -->
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## 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]
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{% set system_injected = namespace(value=false) %}
{% for message in messages %}
{% if message['role'] == 'system' %}
<|im_start|>system
{% if '请逐步推理' in message['content'] or '下面情境' in message['content'] %}
您是一位知识渊博的助手。遇到多项选择题时,您必须在 <think> 标签内进行逐步推理。您的最终答案必须精确地格式化为 \boxed{X},其中 X 是正确选项的字母。
{% elif 'कृपया' in message['content'] %}
आप एक जानकार सहायक हैं। बहुविकल्पीय प्रश्न (MCQ) मिलने पर, आपको अपना चरण-दर-चरण तर्क <think> टैग के अंदर करना होगा। आपका अंतिम उत्तर बिल्कुल \boxed{X} के रूप में होना चाहिए, जहाँ X सही विकल्प का अक्षर है।
{% elif 'Ragiona' in message['content'] or 'Quale è' in message['content'] %}
Sei un assistente esperto. Di fronte a un quesito a risposta multipla, DEVI eseguire il tuo ragionamento passo dopo passo dentro il tag <think>. La risposta finale DEVE essere formattata esattamente come \boxed{X} dove X è la lettera corretta.
{% elif 'Razona' in message['content'] %}
Eres un asistente experto. Ante una pregunta de opción múltiple, DEBES realizar tu razonamiento paso a paso dentro de la etiqueta <think>. Tu respuesta final DEBE aparecer exactamente como \boxed{X} donde X es la letra correcta.
{% elif 'Рассуждайте' in message['content'] %}
Вы — знающий помощник. При получении вопроса с множественным выбором вы ДОЛЖНЫ вести пошаговое рассуждение внутри тега <think>. Окончательный ответ должен быть в формате \boxed{X}, где X — правильная буква варианта.
{% else %}
You are a knowledgeable assistant. When given an MCQ, you MUST perform your step-by-step reasoning INSIDE the <think> tag. Your final answer MUST be formatted exactly as \boxed{X} where X is the correct option letter.
{% endif %}
<|im_end|>
{{ '
' }}
{% set system_injected.value = true %}
{% else %}
{% if not system_injected.value %}
<|im_start|>system
You are a knowledgeable assistant. When given an MCQ, you MUST perform your step-by-step reasoning INSIDE the <think> tag. Your final answer MUST be formatted exactly as \boxed{X} where X is the correct option letter.<|im_end|>
{{ '
' }}
{% set system_injected.value = true %}
{% endif %}
<|im_start|>{{ message['role'] }}
{{ message['content'] }}<|im_end|>
{{ '
' }}
{% endif %}
{% endfor %}
{% if add_generation_prompt %}<|im_start|>assistant
<think>
{% endif %}

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{
"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": null,
"dtype": "bfloat16",
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 6144,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
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"full_attention",
"full_attention"
],
"max_position_embeddings": 40960,
"max_window_layers": 28,
"model_type": "qwen3",
"num_attention_heads": 16,
"num_hidden_layers": 28,
"num_key_value_heads": 8,
"pad_token_id": 151643,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 1000000,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "5.7.0",
"use_cache": false,
"use_sliding_window": false,
"vocab_size": 151936
}

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{
"bos_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"pad_token_id": 151643,
"temperature": 0.6,
"top_k": 20,
"top_p": 0.95,
"transformers_version": "5.7.0"
}

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size 3441185608

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size 11422650

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{
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
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"<|image_pad|>",
"<|video_pad|>"
],
"is_local": true,
"local_files_only": false,
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"padding_side": "left",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}