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Model: aisingapore/Qwen-SEA-LION-v4-8B-VL
Source: Original Platform

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LICENSE

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+MIT License
+
+Copyright 2023 AI Singapore
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+language:
+- en
+- vi
+- id
+- th
+- my
+- ta
+- tl
+- ms
+base_model:
+- Qwen/Qwen3-VL-8B-Instruct
+new_version: aisingapore/Qwen-SEA-LION-v4.5-27B-IT
+base_model_relation: finetune
+library_name: transformers
+pipeline_tag: image-text-to-text
+license: mit
+---
+![Banner!](SEA-LIONv4Qwen-8B-VL.png "v4-banner-Qwen-VL-8B")
+## Qwen-SEA-LION-v4-8B-VL (Instruct)
+
+
+Last update: 2025-12-1
+
+SEA-LION is a collection of Large Language Models (LLMs) which have been pretrained and instruct-tuned for the Southeast Asia (SEA) region.
+
+**Qwen-SEA-LION-v4-8B-VL** is a 8-billion parameter Vision-Language Model (VLM) built upon the Qwen3-VL-8B-Instruct architecture. To ensure **domain adaptation** for the region, the model underwent rigorous supervised fine-tuning (SFT) on a curated dataset of approximately **9 million** instruction-text pairs. This extensive post-training instills **multilingual** and **multicultural** fluency, covering English and 7 key SEA languages: Burmese, Indonesian, Filipino, Malay, Tamil, Thai, and Vietnamese.
+
+Qwen-SEA-LION-v4-8B-VL inherits the following features from Qwen3-VL:
+
+- Long-Context Multimodal Architecture (Native 256K context window)
+- Edge-Optimized Inference (Resource Efficient)
+- Enhanced Vision-Language Capabilities
+- Tool Use
+
+## Model Details
+
+### Model Description
+
+SEA-LION stands for *Southeast Asian Languages In One Network*.
+
+We performed post-training in English and SEA languages on Qwen3-VL-8B-Instruct, a decoder model using the Qwen3 architecture, to create Qwen-SEA-LION-v4-8B-VL.
+
+For tokenization, the model employs the default tokenizer used in Qwen3-VL.
+
+- **Developed by:** AI Products Pillar, AI Singapore
+- **Funded by:** Singapore NRF
+- **Shared by:** AI Products Pillar, AI Singapore
+- **Model type:** Decoder
+- **Context length:** 256k
+- **Language(s):** fine-tuned on Burmese, Indonesian, Filipino, Malay, Tamil, Thai, and Vietnamese
+- **License:** [MIT](https://mit-license.org/)
+- **Finetuned from model:** [Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct)
+
+### Model Sources
+
+- **Repository:** [SEA-LION v4 - an aisingapore Collection](https://huggingface.co/collections/aisingapore/sea-lion-v4)
+
+## Uses
+
+### Out-of-Scope Use
+
+The model has not been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claims, damages, or other liabilities arising from the use of the released weights and codes.
+
+## Bias, Risks, and Limitations
+
+*The model was not tested for robustness against adversarial prompting.* It is important for users to be aware that our model exhibits certain limitations that warrant consideration. Like many LLMs, the model can hallucinate and occasionally generates irrelevant content, introducing fictional elements that are not grounded in the provided context. Users should also exercise caution in interpreting and validating the model's responses due to the potential inconsistencies.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model with 🤗 Transformers libraries.
+
+```
+  pip install transformers>=4.57.0
+
+```
+
+```
+from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
+
+# default: Load the model on the available device(s)
+model = Qwen3VLForConditionalGeneration.from_pretrained(
+    "aisingapore/Qwen-SEA-LION-v4-8B-VL", dtype="auto", device_map="auto"
+)
+
+# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
+# model = Qwen3VLForConditionalGeneration.from_pretrained(
+#     "aisingapore/Qwen-SEA-LION-v4-8B-VL",
+#     dtype=torch.bfloat16,
+#     attn_implementation="flash_attention_2",
+#     device_map="auto",
+# )
+
+processor = AutoProcessor.from_pretrained("aisingapore/Qwen-SEA-LION-v4-8B-VL")
+
+messages = [
+    {
+        "role": "system",
+        "content": [{"type": "text", "text": "You are a helpful assistant."}]
+    },
+    {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "Write a poem on southeast asian countries in Indonesian."}
+        ],
+    }
+]
+
+# Preparation for inference
+inputs = processor.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_dict=True,
+    return_tensors="pt"
+)
+inputs = inputs.to(model.device)
+
+# Inference: Generation of the output
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+
+```
+## Training Details
+
+### Training Data
+
+The instruction fine-tuning text dataset comprises of a collection of OSS & synthetic data.
+
+### Training Procedure
+
+#### Training Hyperparameters
+
+- **Training regime:** Our workflow consists of instruction fine-tuning and model merging.
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+We evaluated Qwen-SEA-LION-v4-8B-VL on general language, multi-turn chat and instruction-following capabilities.
+
+#### Testing Data
+
+General language capabilities
+
+For the evaluation of general language capabilities, we employed the [SEA-HELM evaluation benchmark](https://arxiv.org/abs/2502.14301) across a variety of tasks. These tasks include Question Answering (QA), Sentiment Analysis (Sentiment), Toxicity Detection (Toxicity), Translation in both directions (Eng>Lang & Lang>Eng), Abstractive Summarisation (Abssum), Causal Reasoning (Causal), Natural Language Inference (NLI), Linguistic Diagnostics (LINDSEA), Cultural Knowledge (Kalahi) and Global MMLU Lite.
+
+Instruction-following and Multi-turn Chat
+
+We evaluated the models on instruction-following and multi-turn chat capabilities with SEA-IFEval (based on [IFEval](https://arxiv.org/abs/2311.07911)) and SEA-MTBench (based on [MT-Bench](https://arxiv.org/abs/2306.05685)) respectively. The two datasets were originally in English, the linguists and native speakers in the team worked together to filter, localise and translate the datasets into the respective target languages to ensure that the examples remained reasonable, meaningful and natural.
+
+#### Factors
+
+All evaluations were run with the model specific generation parameters defined in the model config. Each evaluation comprised of 8 runs with different seeds and the final results were averaged across these runs.
+
+For all tasks, the model was expected to provide an answer tag from which the answer was automatically extracted. For tasks where options were provided, the answer should comprise one of the pre-defined options.
+
+The evaluation was done **zero-shot** with native prompts on a sample of 100-1000 instances for each dataset.
+
+SEA-IFEval
+
+SEA-IFEval evaluates a model's ability to adhere to constraints provided in the prompt, for example beginning a response with a specific word/phrase or answering with a certain number of sections. Additionally, accuracy is normalised by the proportion of responses in the correct language (if the model performs the task correctly but responds in the wrong language, it is judged to have failed the task).
+
+SEA-MTBench
+
+SEA-MTBench evaluates a model's ability to engage in multi-turn (2 turns) conversations and respond in ways that align with human needs. We use `gpt-4.1-2025-04-14` as the judge model and compare against `gpt-4.1-2025-04-14` as the baseline model. The metric used is the weighted win rate against the baseline model (i.e. average win rate across each category: Math, Reasoning, STEM, Humanities, Roleplay, Writing, Extraction).
+
+#### Metrics
+
+The following metrics were used for text capabilities:
+
+| **Task** | **Metric** |
+| --- | --- |
+| Sentiment Analysis | Accuracy |
+| Extractive QA (ID, VI, TH, TA) | ChrF++ |
+| MCQ-QA (TL, MY, MS) | Accuracy |
+| Metaphor | Accuracy |
+| Abstractive Summarisation | Rouge-L |
+| Translations | MetricX-24 score (with reference) |
+| Causal Reasoning | Accuracy |
+| Natural Language Inference | Accuracy |
+| LINDSEA | Accuracy |
+| Global MMLU Lite | Accuracy |
+| ThaiExam | Accuracy |
+| Kalahi | Accuracy |
+| SEA-IFEval | Accuracy |
+| SEA-MTBench | Win rate against a reference |
+
+### Results
+
+![Text_result!](results-v4-8B-txt.png "SEA-HELM results for model size <10B only")
+<!-- Provide a leaderboard snapshot of what the model rank is/does. -->
+For details on Qwen-SEA-LION-v4-8B-VL performance, please refer to the SEA-HELM leaderboard, <https://leaderboard.sea-lion.ai/>.
+
+### Retaining VL Capabilities
+
+We also evaluated our models on two types of tasks using datasets specifically focused on Southeast Asian examples to benchmark and compared our models' performances against the original base models (Qwen3-VL-4B/8B).
+
+- Visual Question Answering (VQA): We utilised Multiple Choice Question (MCQ) style tasks, including MARVL, CVQA, and WorldCuisines.
+- Image Captioning: We employed the XM3600 dataset, evaluating strictly on examples relevant to the SEA region.
+
+Key Insight: Despite our fine-tuning process focusing primarily on text data (approximately 9 million regional Q&A and instruction pairs), our evaluations confirm that Qwen-SEA-LION-v4 (4B/8B) successfully retains the high-performance vision-language capabilities of the original base models.
+
+#### Factors
+
+The evaluation was done **zero-shot** with native prompts.
+
+#### Metrics
+
+The following metrics were used to measure performance:
+
+- **Normalised accuracy** was the primary metric for the VQA tasks (CVQA, MARVL, and WorldCuisines).
+- **RefCLIP Score** was used for the XM3600 image captioning task.
+
+### Results
+
+![VL_result!](results-v4-8B-VL.png "The VL capabilities were retained despite training using text only.")
+
+
+<!-- ## 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:** Nvidia H200 140GB GPUs
+- **Hours used:** ??? hrs 
+- **Cloud Provider:** SMC H200
+- **Compute Region:** Singapore
+- **Carbon Emitted:** appx. ??? kg CO2 e -->
+
+## More Information
+
+This is the repository for the commercial instruction-tuned model. The model has *not* been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claims, damages, or other liabilities arising from the use of the released weights and codes.
+
+For more info, please contact us at [sealion@aisingapore.org](mailto:sealion@aisingapore.org)
+
+## Team
+
+Ahn Jeongmi, Antonyrex Sajeban, Chan Hok Teng Adwin, Cheng Zi Yi Nicholas, Choa Hsueh Mei Esther, Heng Jonathan, Huang Yuli, Jann Railey Estrada Montalan, Lau Wayne, Lee Chwan Ren, Leong Wai Yi, Leong Wei Qi, Liew Rachel, Limkonchotiwat Peerat, Muhammad Ridzuan Bin Mokhtar, Nagarajan Karthik, Ng Boon Cheong Raymond, Ngee Chia Tai, Ngui Jian Gang, Nguyen Thanh Ngan, Ong Tat-Wee David, Ong Zhi Hao, Pereira Mark, Poon Joseph, Rengarajan Hamsawardhini, Siow Wei Kang Bryan, Susanto Yosephine, Sutaveephamochanon Anocha, Tan Choon Meng, Tan Chor Phin Evelyn, Tan Siao Wei Jessica, Tan Yixian, Tee Jun Yun, Teng Kok Wai Walter, Teo Eng Sipp Leslie, Tjhi William, Yeo Yeow Tong, Yong Xianbin, Zhang Zhou
+
+## Acknowledgement
+
+This project is supported by the National Research Foundation Singapore and Infocomm Media Development Authority (IMDA), 
+Singapore under its National Large Language Model Funding Initiative.
+
+
+## Contact
+
+[sealion@aisingapore.org](mailto:sealion@aisingapore.org)

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video_preprocessor_config.json

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