# Install dependencies
!pip install transformers==4.31.0 sentence_transformers==2.2.2
from transformers import AutoModelForCausalLM, AutoTokenizer

# 1. Load the model
loaded_model_path = r"path_to_downloaded_model"
model = AutoModelForCausalLM.from_pretrained(loaded_model_path)

# 2. Initialize the tokenizer
tokenizer = AutoTokenizer.from_pretrained(loaded_model_path)

# 3. Prepare input
context = "The context you want to provide to the model."
question = "The question you want to ask the model."
input_text = f"{context}\nQuestion: {question}\n"

# 4. Tokenize input
inputs = tokenizer(input_text, return_tensors="pt")

# 5. Model inference
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_length=512,  # Adjust max_length as per your need
        temperature=0.7,  # Adjust temperature for randomness in sampling
        top_p=0.9,  # Adjust top_p for nucleus sampling
        num_return_sequences=1  # Number of sequences to generate
    )

# 6. Decode and print the output
generated_texts = [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]
print("Generated Texts:")
for text in generated_texts:
    print(text)

pradhaph/medical-falcon-7b

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pradhaph/
medical-falcon-7b