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--- |
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base_model: meta-llama/Meta-Llama-3-8B-Instruct |
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library_name: peft |
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license: other |
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metrics: |
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- accuracy |
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tags: |
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- llama-factory |
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- lora |
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- generated_from_trainer |
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model-index: |
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- name: sft |
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results: [] |
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--- |
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# Supervised Fine-Tuned Model |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the open_platypus dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6769 |
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- Accuracy: 0.8116 |
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## Model description |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the open_platypus dataset. |
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## Intended uses & limitations |
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### How to use |
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You can use this model directly with a pipeline for text classification. Here is an example: |
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```python |
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import logging |
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from transformers import pipeline |
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# Set up logging |
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logging.basicConfig(filename='model_output.log', level=logging.INFO) |
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# Load the model using the pipeline API for text generation |
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model_name = "2nji/llama3-platypus" |
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generator = pipeline('text-generation', model=model_name) |
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# Example prompt |
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prompt = "Hello! How can AI help humans in daily life?" |
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# Generate response |
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try: |
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responses = generator(prompt, max_length=50) # Adjust max_length as needed |
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response_text = responses[0]['generated_text'] |
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print("Model response:", response_text) |
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# Log the output |
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logging.info("Sent prompt: %s", prompt) |
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logging.info("Received response: %s", response_text) |
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except Exception as e: |
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logging.error("Error in generating response: %s", str(e)) |
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``` |
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## Training and evaluation data |
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The model was fine-tuned on the open_platypus dataset. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 3.0 |
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### Training results |
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The model was trained on a single NVIDIA H100 GPU with the following results: |
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- Loss: 0.6769 |
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- Accuracy: 0.8116 |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.42.3 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |