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README.md
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<!-- Provide a quick summary of what the model is/does. -->
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### Model Description
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generated_text = wrapped_tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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print(generated_text)
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```
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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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[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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[More Information Needed]
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### Results
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[More Information Needed]
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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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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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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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[More Information Needed]
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**APA:**
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[More Information Needed]
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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 Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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## Model Details
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- `Tokenizer`
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```py
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from transformers import PreTrainedTokenizerFast
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# Assuming your custom tokenizer is `tokenizer`
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wrapped_tokenizer = PreTrainedTokenizerFast(
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tokenizer_object=tokenizer,
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bos_token="[BOS]", # Replace with your special tokens
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eos_token="[EOS]", # Replace with your special tokens
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unk_token="[UNK]",
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pad_token="[PAD]"
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)
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# Ensure padding is applied to the right side (used in causal language modeling)
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wrapped_tokenizer.padding_side = "right"
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```
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- `Model`
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```py
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from transformers import LlamaConfig, LlamaForCausalLM
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config = LlamaConfig(
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vocab_size=len(wrapped_tokenizer), # Get vocab size from the wrapped tokenizer
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hidden_size=512, # Adjust model size as needed
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intermediate_size=1024,
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num_hidden_layers=8, # Set number of layers and heads
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num_attention_heads=8,
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max_position_embeddings=512,
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rms_norm_eps=1e-6,
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initializer_range=0.02,
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use_cache=True,
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pad_token_id=wrapped_tokenizer.pad_token_id,
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bos_token_id=wrapped_tokenizer.bos_token_id,
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eos_token_id=wrapped_tokenizer.eos_token_id,
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)
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model = LlamaForCausalLM(config)
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```
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- `Trainer`
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```py
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from transformers import TrainingArguments, Trainer
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# Define training arguments
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training_args = TrainingArguments(
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output_dir="kongo-llama", # Output directory for model and checkpoints
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num_train_epochs=1,
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per_device_train_batch_size=8,
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learning_rate=5e-5,
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warmup_steps=500,
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weight_decay=0.01,
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logging_dir="./logs",
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logging_steps=10,
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save_steps=1000,
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)
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trainer = Trainer(
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model=model, # Your model instance
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args=training_args, # Training arguments
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train_dataset=dataset, # Tokenized dataset with input_ids and labels
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tokenizer=wrapped_tokenizer, # Wrapped tokenizer
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data_collator=data_collator, # Data collator for causal language modeling
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)
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````
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### Model Description
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generated_text = wrapped_tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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print(generated_text)
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```
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