Update README.md

README.md

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 library_name: peft
 base_model: unsloth/gemma-7b-bnb-4bit
 ---
-
-# 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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-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
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-### Model Sources [optional]
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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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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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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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-## 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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-### Training Procedure
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-#### Preprocessing [optional]
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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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-## 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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-#### Factors
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-#### Metrics
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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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-## 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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-## 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
+prompt
+
+```
+<original>Ok. What do the drivers look like?</original>
+<translate to="th">
+```
+
+response
+```
+<original>กรุงเทพอยู่ที่ไหน</original>
+<translate to="en">where is bangkok</translate><eos>
+```
+
+code to create dataset
+```python
+import random
+
+
+alpaca_prompt = """<original>{}</original>
+<translate to="{}">{}"""
+
+BOS_TOKEN = tokenizer.bos_token # Must add EOS_TOKEN
+EOS_TOKEN = "</translate>"+tokenizer.eos_token # Must add EOS_TOKEN
+def formatting_prompts_func(examples):
+    translations = examples["translation"]
+    texts = []
+    text_en = ""
+    text_th = ""
+    translate_to = 'th'
+    max_group_count = 1
+    group_count = 0
+    for translation in translations:
+       
+        if group_count >= max_group_count:
+            if(translate_to == 'th'):
+                text = alpaca_prompt.format(text_en, translate_to, text_th) + EOS_TOKEN
+            else:
+                text = alpaca_prompt.format(text_th, translate_to, text_en) + EOS_TOKEN
+            texts.append(text)
+            text_en = ""
+            text_th = ""
+            max_group_count = random.randint(1, 5)
+            group_count = 0
+            translate_to = random.choice(['en', 'th'])
+        
+        num_newlines = random.randint(1, 5)
+        newlines = '\n' * num_newlines
+        if(text_en == ""):
+            text_en = translation['en']
+            text_th = translation['th']
+        else:
+            text_en = text_en+newlines+translation['en']
+            text_th = text_th+newlines+translation['th']
+        group_count = group_count+1
+    if(translate_to == 'th'):
+        text = alpaca_prompt.format(text_en, translate_to, text_th) + EOS_TOKEN
+    else:
+        text = alpaca_prompt.format(text_th, translate_to, text_en) + EOS_TOKEN
+    texts.append(text)
+    return { "text" : texts, }
+
+
+from datasets import load_dataset
+import datasets
+
+# dataset = load_dataset("scb_mt_enth_2020",'enth', download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD,cache_dir ="./cache")
+dataset = load_dataset("scb_mt_enth_2020",'enth',cache_dir ="./cache")
+dataset = dataset.shuffle(seed=42)
+dataset = dataset.map(formatting_prompts_func, batched = True,remove_columns=["translation",'subdataset'])
+dataset['train'][0:5]
+```
 
 [More Information Needed]
 ### Framework versions