Xennon-BD
/

Doctor-Chad

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-# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
-# Doc / guide: https://huggingface.co/docs/hub/model-cards
-{}
 ---
-# Model Card for Model ID
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-## Model Details
-### Model Description
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-Use the code below to get started with the model.
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 ---
+license: mit
+datasets:
+- avaliev/chat_doctor
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- medical
+- biology
+- conversetional
+- qween
+- doctor
 ---
+To generate text using the `AutoTokenizer` and `AutoModelForCausalLM` from the Hugging Face Transformers library, you can follow these steps. First, ensure you have the necessary libraries installed:
+```bash
+pip install transformers torch
+```
+Then, use the following Python code to load the model and generate text:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("Xennon-BD/Doctor-Chad")
+model = AutoModelForCausalLM.from_pretrained("Xennon-BD/Doctor-Chad")
+# Define the input prompt
+input_text = "Hello, how are you doing today?"
+# Encode the input text
+input_ids = tokenizer.encode(input_text, return_tensors="pt")
+# Generate text
+output_ids = model.generate(input_ids, max_length=50, num_return_sequences=1, do_sample=True)
+# Decode the generated text
+generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+print(generated_text)
+```
+### Explanation:
+1. **Load the Tokenizer and Model**:
+   ```python
+   tokenizer = AutoTokenizer.from_pretrained("Xennon-BD/Doctor-Chad")
+   model = AutoModelForCausalLM.from_pretrained("Xennon-BD/Doctor-Chad")
+   ```
+   This code loads the tokenizer and model from the specified Hugging Face model repository.
+2. **Define the Input Prompt**:
+   ```python
+   input_text = "Hello, how are you doing today?"
+   ```
+   This is the text prompt that you want the model to complete or generate text from.
+3. **Encode the Input Text**:
+   ```python
+   input_ids = tokenizer.encode(input_text, return_tensors="pt")
+   ```
+   The `tokenizer.encode` method converts the input text into token IDs that the model can process. The `return_tensors="pt"` argument specifies that the output should be in the form of PyTorch tensors.
+4. **Generate Text**:
+   ```python
+   output_ids = model.generate(input_ids, max_length=50, num_return_sequences=1, do_sample=True)
+   ```
+   The `model.generate` method generates text based on the input token IDs.
+   - `max_length=50` specifies the maximum length of the generated text.
+   - `num_return_sequences=1` specifies the number of generated text sequences to return.
+   - `do_sample=True` indicates that sampling should be used to generate text, which introduces some randomness and can produce more varied text.
+5. **Decode the Generated Text**:
+   ```python
+   generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+   ```
+   The `tokenizer.decode` method converts the generated token IDs back into human-readable text. The `skip_special_tokens=True` argument ensures that special tokens (like `<|endoftext|>`) are not included in the output.
+6. **Print the Generated Text**:
+   ```python
+   print(generated_text)
+   ```
+   This prints the generated text to the console.
+You can modify the input prompt and the parameters of the `model.generate` method to suit your needs, such as adjusting `max_length` for longer or shorter text generation, or changing `num_return_sequences` to generate multiple variations.