pythainlp

This is a LoRA adapter for Thai poems trained on 80k poems to be used with WangChanGLM. WangChanGLM is a multilingual, instruction-finetuned Facebook XGLM-7.5B using open-source, commercially permissible datasets (LAION OIG chip2 and infilldbpedia, 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) and another checkpoint further finetuned on Google-Translated Thai dataset (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 in our Github, as well as datasets and model weights on HuggingFace. In a similar manner to Dolly v2, we use only use open-source, commercially permissive pretrained models and datasets, our models are neither restricted by non-commercial clause like models that use LLaMA as base nor non-compete clause like models that use self-instruct datasets from ChatGPT. - Developed by: PyThaiNLP and VISTEC-depa AI Research Institute of Thailand - Model type: Finetuned XGLM-7.5B - Language(s) (NLP): `en`, `th`, `ja`, `vi` capacibilities evaluated, theoretically all 30 languages of XGLM-7.5B - License: CC-BY SA 4.0 - Repository: pythainlp/wangchanglm - Blog: Medium - Demo: Colab notebook Intended to be use as an instruction-following model for reading comprehension, brainstorming and creative writing. The model can be finetuned for any typical instruction-following use cases. We do not expect the models to perform well in math problems, reasoning, and factfulness. We intentionally filter out training examples from these use cases. We noticed similar limitations to other finetuned instruction followers such as math problems, reasoning, and factfulness. Even though the models do not perform on the level that we expect them to be abused, they do contain undesirable biases and toxicity and should be further optimized for your particular use cases. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. - Training regime: LoRA with 4 GPUs. See more details at pythainlp/wangchanglm. We performed automatic evaluation in the style of Vicuna and human evaluation. See more details from our [blog](). Experiments were conducted using a private infrastructure, which has a carbon efficiency of 0.432 kgCO2eq/kWh. A cumulative of 500 hours of computation was performed on hardware of type Tesla V100-SXM2-32GB (TDP of 300W). Total emissions are estimated to be 64.8 CO2eq of which 0 percents were directly offset. Estimations were conducted using the MachineLearning Impact calculator.