# Model Card for WangChanGLM 🐘 - The Multilingual Instruction-Following Model

<!-- Provide a longer summary of what this model is. -->

WangChanGLM is a 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). In a similar manner to [Dolly v2](https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm), 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. See our live demo [here]().

- **Developed by:** [PyThaiNLP](https://www.github.com/pythainlp) and [VISTEC-depa AI Research Institute of Thailand](https://huggingface.co/airesearch)
- **Model type:** Finetuned [XGLM-7.5B](https://huggingface.co/facebook/xglm-7.5B)
- **Language(s) (NLP)**: `en`, `th`, `ja`, `vi` capacibilities evaluated, theoretically all 30 languages of [XGLM-7.5B](https://huggingface.co/facebook/xglm-7.5B)
- **License:** [CC-BY SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** [pythainlp/wangchanglm](https://www.github.com/pythainlp/wangchanglm)
- **Blog:** [Medium](https://link.medium.com/s2MWr3ZXnzb)
- **Demo:** [Colab notebook](https://colab.research.google.com/github/pythainlp/WangChanGLM/blob/main/demo/WangChanGLM_v0_1_demo.ipynb)

## 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

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

Intended to be use as an instruction-following model for reading comprehension, brainstorming and creative writing.

### Downstream Use

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

The model can be finetuned for any typical instruction-following use cases.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

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.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

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.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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.

```
model_name = "pythainlp/wangchanglm-7.5B-sft-en"
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    return_dict=True, 
    load_in_8bit=True ,
    device_map="auto", 
    torch_dtype=torch.float16, 
    offload_folder="./", 
    low_cpu_mem_usage=True,
)
text = "เล่นหุ้นยังไงให้รวย"
tokenizer = AutoTokenizer.from_pretrained(model_name)
batch = tokenizer(text, return_tensors="pt")
with torch.cuda.amp.autocast(): 
  output_tokens = model.generate(
      input_ids=batch["input_ids"],
      max_new_tokens=max_gen_len, # 512
      begin_suppress_tokens = exclude_ids,
      no_repeat_ngram_size=2,
      
      #oasst k50
      top_k=50,
      top_p=top_p, # 0.95
      typical_p=1.,
      temperature=temperature, # 0.9
      
      # #oasst typical3
      # typical_p = 0.3,
      # temperature = 0.8,
      # repetition_penalty = 1.2,
  )
tokenizer.decode(output_tokens[0], skip_special_tokens=True)
```

## Training Details

### Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

Finetuning datasets are sourced from [LAION OIG chip2 and infill_dbpedia](https://huggingface.co/datasets/laion/OIG) ([Apache-2.0](https://github.com/pythainlp/wangchanglm/blob/main/LICENSE)), [DataBricks Dolly v2](https://github.com/databrickslabs/dolly) ([Apache-2.0](https://github.com/pythainlp/wangchanglm/blob/main/LICENSE)), [OpenAI TL;DR](https://github.com/openai/summarize-from-feedback) ([MIT](https://opensource.org/license/mit/)), and [Hello-SimpleAI HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3) ([CC-BY SA](https://creativecommons.org/licenses/by-sa/4.0/)).

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing 

See [pythainlp/wangchanglm](https://www.github.com/pythainlp/wangchanglm).


#### Training Hyperparameters

- **Training regime:** LoRA with 4 GPUs. See more details at [pythainlp/wangchanglm](https://www.github.com/pythainlp/wangchanglm).

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

We performed automatic evaluation in the style of [Vicuna](https://vicuna.lmsys.org/) and human evaluation. See more details from our [blog]().

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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](https://mlco2.github.io/impact#compute).

## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```
@software{charin_polpanumas_2023_7878101,
  author       = {Charin Polpanumas and
                  Wannaphong Phatthiyaphaibun and
                  Patomporn Payoungkhamdee and
                  Peerat Limkonchotiwat and
                  Lalita Lowphansirikul and
                  Can Udomcharoenchaikit and
                  Titipat Achakulwisut and
                  Ekapol Chuangsuwanich and
                  Sarana Nutanong},
  title        = {{WangChanGLM🐘 — The Multilingual Instruction- 
                   Following Model}},
  month        = apr,
  year         = 2023,
  publisher    = {Zenodo},
  version      = {v0.1},
  doi          = {10.5281/zenodo.7878101},
  url          = {https://doi.org/10.5281/zenodo.7878101}
}
```

## Model Card Contact

[PyThaiNLP](https://github.com/pythainlp)
{"language": ["en", "th", "ja", "vi"], "license": "cc-by-sa-4.0", "datasets": ["laion/OIG", "Hello-SimpleAI/HC3", "databricks/databricks-dolly-15k"], "pipeline_tag": "text-generation"}
transformers pytorch xglm text-generation en th ja vi dataset:laion/OIG dataset:Hello-SimpleAI/HC3 dataset:databricks/databricks-dolly-15k license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #xglm #text-generation #en #th #ja #vi #dataset-laion/OIG #dataset-Hello-SimpleAI/HC3 #dataset-databricks/databricks-dolly-15k #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

# Model Card for WangChanGLM  - The Multilingual Instruction-Following Model



WangChanGLM is a 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) 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. See our live demo [here]().

- 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

### Model Sources



- Repository: pythainlp/wangchanglm
- Blog: Medium
- Demo: Colab notebook

## Uses



### Direct Use



Intended to be use as an instruction-following model for reading comprehension, brainstorming and creative writing.

### Downstream Use



The model can be finetuned for any typical instruction-following use cases.

### Out-of-Scope Use



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.

## Bias, Risks, and Limitations



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.

### Recommendations



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.



## Training Details

### Training Data



Finetuning datasets are sourced from LAION OIG chip2 and infill_dbpedia (Apache-2.0), DataBricks Dolly v2 (Apache-2.0), OpenAI TL;DR (MIT), and Hello-SimpleAI HC3 (CC-BY SA).

### Training Procedure 



#### Preprocessing 

See pythainlp/wangchanglm.


#### Training Hyperparameters

- Training regime: LoRA with 4 GPUs. See more details at pythainlp/wangchanglm.

## Evaluation



We performed automatic evaluation in the style of Vicuna and human evaluation. See more details from our [blog]().

## Environmental Impact



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.

BibTeX:



## Model Card Contact

PyThaiNLP
# Model Card for WangChanGLM  - The Multilingual Instruction-Following Model



WangChanGLM is a 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) 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. See our live demo [here]().

- 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 ### Model Sources



- Repository: pythainlp/wangchanglm
- Blog: Medium
- Demo: Colab notebook ## Uses ### Direct Use



Intended to be use as an instruction-following model for reading comprehension, brainstorming and creative writing. ### Downstream Use



The model can be finetuned for any typical instruction-following use cases. ### Out-of-Scope Use



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. ## Bias, Risks, and Limitations



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. ### Recommendations



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. ## Training Details ### Training Data



Finetuning datasets are sourced from LAION OIG chip2 and infill_dbpedia (Apache-2.0), DataBricks Dolly v2 (Apache-2.0), OpenAI TL;DR (MIT), and Hello-SimpleAI HC3 (CC-BY SA). ### Training Procedure #### Preprocessing 

See pythainlp/wangchanglm. #### Training Hyperparameters

- Training regime: LoRA with 4 GPUs. See more details at pythainlp/wangchanglm. ## Evaluation



We performed automatic evaluation in the style of Vicuna and human evaluation. See more details from our [blog](). ## Environmental Impact



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.

BibTeX: ## Model Card Contact

PyThaiNLP
TAGS
#transformers #pytorch #xglm #text-generation #en #th #ja #vi #dataset-laion/OIG #dataset-Hello-SimpleAI/HC3 #dataset-databricks/databricks-dolly-15k #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Card for WangChanGLM  - The Multilingual Instruction-Following Model



WangChanGLM is a 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) 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. See our live demo [here]().

- 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 ### Model Sources



- Repository: pythainlp/wangchanglm
- Blog: Medium
- Demo: Colab notebook ## Uses ### Direct Use



Intended to be use as an instruction-following model for reading comprehension, brainstorming and creative writing. ### Downstream Use



The model can be finetuned for any typical instruction-following use cases. ### Out-of-Scope Use



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. ## Bias, Risks, and Limitations



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. ### Recommendations



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. ## Training Details ### Training Data



Finetuning datasets are sourced from LAION OIG chip2 and infill_dbpedia (Apache-2.0), DataBricks Dolly v2 (Apache-2.0), OpenAI TL;DR (MIT), and Hello-SimpleAI HC3 (CC-BY SA). ### Training Procedure #### Preprocessing 

See pythainlp/wangchanglm. #### Training Hyperparameters

- Training regime: LoRA with 4 GPUs. See more details at pythainlp/wangchanglm. ## Evaluation



We performed automatic evaluation in the style of Vicuna and human evaluation. See more details from our [blog](). ## Environmental Impact



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.

BibTeX: ## Model Card Contact

PyThaiNLP
TAGS
#transformers #pytorch #xglm #text-generation #en #th #ja #vi #dataset-laion/OIG #dataset-Hello-SimpleAI/HC3 #dataset-databricks/databricks-dolly-15k #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Card for WangChanGLM  - The Multilingual Instruction-Following Model



WangChanGLM is a 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) 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. See our live demo [here]().

- 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### Model Sources



- Repository: pythainlp/wangchanglm
- Blog: Medium
- Demo: Colab notebook## Uses### Direct Use



Intended to be use as an instruction-following model for reading comprehension, brainstorming and creative writing.### Downstream Use



The model can be finetuned for any typical instruction-following use cases.### Out-of-Scope Use



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.## Bias, Risks, and Limitations



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.### Recommendations



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.## Training Details### Training Data



Finetuning datasets are sourced from LAION OIG chip2 and infill_dbpedia (Apache-2.0), DataBricks Dolly v2 (Apache-2.0), OpenAI TL;DR (MIT), and Hello-SimpleAI HC3 (CC-BY SA).### Training Procedure#### Preprocessing 

See pythainlp/wangchanglm.#### Training Hyperparameters

- Training regime: LoRA with 4 GPUs. See more details at pythainlp/wangchanglm.## Evaluation



We performed automatic evaluation in the style of Vicuna and human evaluation. See more details from our [blog]().## Environmental Impact



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.

BibTeX:## Model Card Contact

PyThaiNLP