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--- |
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library_name: transformers |
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license: llama3 |
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base_model: |
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- meta-llama/Meta-Llama-3-8B |
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tags: |
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- LLaMA3 |
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- llama |
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--- |
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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:** Nirajan Dhakal |
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- **Model type:** Text Generation |
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- **Language(s) (NLP):** English |
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- **License:** LLaMA 3 Community License |
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Running Inference: |
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```python |
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# Load model directly |
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline |
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tokenizer = AutoTokenizer.from_pretrained("nirajandhakal/LLaMA3-Reasoning") |
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model = AutoModelForCausalLM.from_pretrained("nirajandhakal/LLaMA3-Reasoning") |
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pipe = pipeline("text-generation", model="nirajandhakal/LLaMA3-Reasoning", truncation=True) |
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# Define a prompt for the model |
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prompt = "What are the benefits of using artificial intelligence in healthcare?" |
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# Generate text based on the prompt |
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generated_text = pipe(prompt, max_length=200) |
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# Print the generated text |
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print(generated_text[0]['generated_text']) |
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``` |
