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# Model Card for Model ID
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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]
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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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---
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# Model Card for Model ID
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## How to use
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Loading the model from Hunggingface:
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("ZiweiChen/BioMistral-Clinical-7B")
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model = AutoModelForCausalLM.from_pretrained("ZiweiChen/BioMistral-Clinical-7B")
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```
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Lightweight model loading can be used - using 4-bit quantization!
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```python
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from transformers import AutoTokenizer, BitsAndBytesConfig, AutoModelForCausalLM
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import torch
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16
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)
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tokenizer = AutoTokenizer.from_pretrained("ZiweiChen/BioMistral-Clinical-7B")
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model = AutoModelForCausalLM.from_pretrained("ZiweiChen/BioMistral-Clinical-7B", quantization_config=bnb_config)
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```
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How to Generate text:
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```python
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model_device = next(model.parameters()).device
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prompt = """
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How to treat severe obesity?
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"""
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model_input = tokenizer(prompt, return_tensors="pt").to(model_device)
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with torch.no_grad():
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output = model.generate(**model_input, max_new_tokens=100)
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answer = tokenizer.decode(output[0], skip_special_tokens=True)
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print(answer)
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```
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## Model Details
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### Model Description
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