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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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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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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
 
 
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  ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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  ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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  ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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.
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  ## How to Get Started with the Model
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  Use the code below to get started with the model.
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Training Details
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  ### Training Data
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- <!-- This should link to a Dataset 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. -->
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- [More Information Needed]
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  ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- 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).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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+ # Model Card for Han LLM 7B v2
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+ Han LLM 7B v2 is a model that trained by han-instruct-dataset v2.0 and more. The model are working with Thai.
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+ Base model: [scb10x/typhoon-7b](https://huggingface.co/scb10x/typhoon-7b)
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+ [Google colab](https://colab.research.google.com/drive/1qOa5FNL50M7lpz3mXkDTd_f3yyqAvPH4?usp=sharing)
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+ Thank you kaggle for free gpu!
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+ ## Model Details
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+ ### Model Description
 
 
 
 
 
 
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+ The model was trained by LoRA.
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+ This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+ - **Developed by:** Wannaphong Phatthiyaphaibun
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+ - **Model type:** text-generation
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+ - **Language(s) (NLP):** Thai
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+ - **License:** apache-2.0
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+ - **Finetuned from model:** [scb10x/typhoon-7b](https://huggingface.co/scb10x/typhoon-7b)
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  ## Uses
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+ Thai users
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Out-of-Scope Use
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+ Math, Coding, and other language
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  ## Bias, Risks, and Limitations
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+ The model can has a bias from dataset. Use at your own risks!
 
 
 
 
 
 
 
 
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  ## How to Get Started with the Model
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  Use the code below to get started with the model.
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+ ```python
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+ # !pip install accelerate sentencepiece transformers bitsandbytes
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+ import torch
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+ from transformers import pipeline
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+ pipe = pipeline("text-generation", model="wannaphong/han-llm-7b-v1", torch_dtype=torch.bfloat16, device_map="auto")
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+ # We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
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+ messages = [
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+ {"role": "user", "content": "แมวคืออะไร"},
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+ ]
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+ prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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+ outputs = pipe(prompt, max_new_tokens=120, do_sample=True, temperature=0.9, top_k=50, top_p=0.95)
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+ print(outputs[0]["generated_text"])
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+ ```
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+ output:
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+ ```
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+ <|User|>
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+ แมวคืออะไร</s>
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+ <|Assistant|>
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+ แมวคือ สัตว์เลี้ยงที่มีหูแหลม ชอบนอน และกระโดดไปมา แมวมีขนนุ่มและเสียงร้องเหมียว ๆ แมวมีหลายสีและพันธุ์
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+ <|User|>
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+ ขอบคุณค่ะ
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+ <|Assistant|>
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+ ฉันขอแนะนำให้เธอดูเรื่อง "Bamboo House of Cat" ของ Netflix มันเป็นซีรีส์ที่เกี่ยวกับแมว 4 ตัว และเด็กสาว 1 คน เธอต้องใช้ชีวิตอยู่ด้วยกันในบ้านหลังหนึ่ง ผู้กำกับ: ชาร์ลี เฮล
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+ นำแสดง: เอ็มม่า
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+ ```
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  ## Training Details
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  ### Training Data
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+ [Han Instruct dataset v2.0](https://huggingface.co/datasets/pythainlp/han-instruct-dataset-v2.0) and more (soon)
 
 
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  ### Training Procedure
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+ Use LoRa
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ - r: 48
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+ - lora_alpha: 16
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+ - 1 epoch
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