ruslanmv
/

ai-medical-model-4bit

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 ---
-library_name: transformers
 tags:
-- unsloth
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-# Model Card for Model ID
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 ---
+language: en
+license: apache-2.0
 tags:
+- text-generation-inference
+- transformers
+- ruslanmv
+- llama
+- trl
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+datasets:
+- ruslanmv/ai-medical-dataset
 ---
+# ai-medical-model-4bit: Fine-Tuned Llama3 for Technical Medical Questions
+[![](future.jpg)](https://ruslanmv.com/)
+This repository provides a fine-tuned version of the powerful Llama3 8B Instruct model, specifically designed to answer medical questions in an informative way.
+It leverages the rich knowledge contained in the AI Medical Dataset ([ruslanmv/ai-medical-chatbot](https://huggingface.co/datasets/ruslanmv/ai-medical-dataset)).
+**Model & Development**
+- **Developed by:** ruslanmv
+- **License:** Apache-2.0
+- **Finetuned from model:** meta-llama/Meta-Llama-3-8B-Instruct
+**Key Features**
+- **Medical Focus:** Optimized to address health-related inquiries.
+- **Knowledge Base:** Trained on a comprehensive medical chatbot dataset.
+- **Text Generation:** Generates informative and potentially helpful responses.
+**Installation**
+This model is accessible through the Hugging Face Transformers library. Install it using pip:
+```bash
+!python -m pip install --upgrade pip
+!pip3 install torch==2.2.1  torchvision torchaudio xformers --index-url https://download.pytorch.org/whl/cu121
+!pip install  bitsandbytes  accelerate
+```
+**Usage Example**
+Here's a Python code snippet demonstrating how to interact with the `ai-medical-model-4bit` model and generate answers to your medical questions:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+model_name = "ruslanmv/ai-medical-model-4bit"
+device_map = 'auto'
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16,
+)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    quantization_config=bnb_config,
+    trust_remote_code=True,
+    use_cache=False,
+    device_map=device_map
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+def askme(question):
+  prompt = f"<|start_header_id|>system<|end_header_id|> You are a Medical AI chatbot assistant. <|eot_id|><|start_header_id|>User: <|end_header_id|>This is the question: {question}<|eot_id|>"
+  # Tokenizing the input and generating the output
+  #prompt = f"{question}"
+  # Tokenizing the input and generating the output
+  inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
+  outputs = model.generate(**inputs, max_new_tokens=256, use_cache=True)
+  answer = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+  # Try Remove the prompt
+  try:
+      # Split the answer at the first line break, assuming system intro and question are on separate lines
+      answer_parts = answer.split("\n", 1)
+      # If there are multiple parts, consider the second part as the answer
+      if len(answer_parts) > 1:
+        answers = answer_parts[1].strip()  # Remove leading/trailing whitespaces
+      else:
+        answers = ""  # If no split possible, set answer to empty string
+      print(f"Answer: {answers}")
+  except:
+      print(answer)
+# Example usage
+# - Question:  Make the question.
+question="What was the main cause of the inflammatory CD4+ T cells?"
+askme(question)
+```
+the type of answer is :
+```
+The main cause of inflammatory CD4+ T cells is typically attributed to an imbalance in the immune system's response to an antigen, leading to an overactive immune response. This can occur due to various factors, such as:
+1. **Autoimmune disorders**: In conditions like rheumatoid arthritis, lupus, or multiple sclerosis, the immune system mistakenly attacks the body's own tissues, leading to chronic inflammation and the activation of CD4+ T cells.
+2. **Infections**: Certain infections, like tuberculosis or HIV, can trigger an excessive immune response, resulting in the activation of CD4+ T cells.
+3. **Environmental factors**: Exposure to pollutants, toxins, or allergens can trigger an immune response, leading to the activation of CD4+ T cells.
+4. **Genetic predisposition**: Some individuals may be more susceptible to developing inflammatory CD4+ T cells due to their genetic makeup.
+5. **Immunosuppression**: Weakened immune systems, such as those resulting from immunosuppressive therapy or HIV/AIDS, can lead to an overactive immune response and the activation of CD4+ T cells.
+These factors can lead to the activation of CD4+
+```
+**Important Note**
+This model is intended for informational purposes only and should not be used as a substitute for professional medical advice. Always consult with a qualified healthcare provider for any medical concerns.
+**License**
+This model is distributed under the Apache License 2.0 (see LICENSE file for details).
+**Contributing**
+We welcome contributions to this repository! If you have improvements or suggestions, feel free to create a pull request.
+**Disclaimer**
+While we strive to provide informative responses, the accuracy of the model's outputs cannot be guaranteed.