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# Model Card for WangChanGLM 🐘 - The Thai-Turned-Multilingual Instruction-Following Model
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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WangChanGLM is a Thai-turned-multilingual, instruction-finetuned Facebook XGLM-7.5B using open-source, commercially permissible datasets (LAION OIG chip2 and infill_dbpedia, DataBricks Dolly v2, OpenAI TL;DR, and Hello-SimpleAI HC3; about 400k examples), released under CC-BY SA 4.0. The models are trained to perform a subset of instruction-following tasks we found most relevant namely: reading comprehension, brainstorming, and creative writing. We provide the weights for a model finetuned on an English-only dataset ([wangchanglm-7.5B-sft-en](https://huggingface.co/pythainlp/wangchanglm-7.5B-sft-en)) and another checkpoint further finetuned on Google-Translated Thai dataset ([wangchanglm-7.5B-sft-enth](https://huggingface.co/pythainlp/wangchanglm-7.5B-sft-enth)). We perform Vicuna-style evaluation using both humans and ChatGPT (in our case, `gpt-3.5-turbo` since we are still on the waitlist for `gpt-4`) and observe some discrepancies between the two types of annoators. All training and evaluation codes are shared under the [Apache-2.0 license](https://github.com/pythainlp/wangchanglm/blob/main/LICENSE) in our Github, as well as datasets and model weights on [HuggingFace](https://huggingface.co/pythainlp). See our live demo [here]().
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# Model Card for WangChanGLM 🐘 - The Thai-Turned-Multilingual Instruction-Following Model
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<!-- Provide a longer summary of what this model is. -->
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WangChanGLM is a Thai-turned-multilingual, instruction-finetuned Facebook XGLM-7.5B using open-source, commercially permissible datasets (LAION OIG chip2 and infill_dbpedia, DataBricks Dolly v2, OpenAI TL;DR, and Hello-SimpleAI HC3; about 400k examples), released under CC-BY SA 4.0. The models are trained to perform a subset of instruction-following tasks we found most relevant namely: reading comprehension, brainstorming, and creative writing. We provide the weights for a model finetuned on an English-only dataset ([wangchanglm-7.5B-sft-en](https://huggingface.co/pythainlp/wangchanglm-7.5B-sft-en)) and another checkpoint further finetuned on Google-Translated Thai dataset ([wangchanglm-7.5B-sft-enth](https://huggingface.co/pythainlp/wangchanglm-7.5B-sft-enth)). We perform Vicuna-style evaluation using both humans and ChatGPT (in our case, `gpt-3.5-turbo` since we are still on the waitlist for `gpt-4`) and observe some discrepancies between the two types of annoators. All training and evaluation codes are shared under the [Apache-2.0 license](https://github.com/pythainlp/wangchanglm/blob/main/LICENSE) in our Github, as well as datasets and model weights on [HuggingFace](https://huggingface.co/pythainlp). See our live demo [here]().
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