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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- text-generation-inference |
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- transformers |
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- ruslanmv |
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- llama |
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- trl |
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base_model: meta-llama/Meta-Llama-3-8B-Instruct |
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datasets: |
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- ruslanmv/ai-medical-dataset |
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widget: |
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- example_title: ai-medical-model-32bit |
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messages: |
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- role: system |
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content: You are an expert and experienced from the healthcare and biomedical |
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domain with extensive medical knowledge and practical experience. |
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- role: user |
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content: What was the main cause of the inflammatory CD4+ T cells? |
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output: |
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text: Answer I'm happy to help! The main cause of inflammatory CD4+ T cells is |
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a complex process that involves multiple factors. However, some of the key triggers |
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include 1. Activation of CD4+ T cells CD4+ T cells are activated by antigens, |
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cytokines, and other signals, leading to their proliferation and differentiation |
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into effector cells. 2. Cytokine production Activated CD4+ T cells produce cytokines |
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such as interleukin-2 (IL-2), interferon-gamma (IFN-γ), and tumor necrosis factor-alpha |
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(TNF-α), which promote inflammation and immune responses. 3. Chemokine production |
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CD4+ T cells also produce chemokines, such as CCL3, CCL4, and CCL5, which attract |
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other immune cells to the site of inflammation. 4. Toll-like receptor (TLR) |
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activation TLRs are pattern recognition receptors that recognize pathogen-associated |
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molecular patterns (PAMPs) and activate CD4+ T cells. 5. Bacterial or viral |
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infections Infections caused by bacteria, viruses, or fungi can trigger the |
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activation of CD4+ T cells and the production of cytokines and chemokines |
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model-index: |
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- name: ai-medical-model-32bit |
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results: [] |
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--- |
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# ai-medical-model-32bit: Fine-Tuned Llama3 for Technical Medical Questions |
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[](https://ruslanmv.com/) |
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This repository provides a fine-tuned version of the powerful Llama3 8B Instruct model, specifically designed to answer medical questions in an informative way. |
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It leverages the rich knowledge contained in the AI Medical Dataset ([ruslanmv/ai-medical-dataset](https://huggingface.co/datasets/ruslanmv/ai-medical-dataset)). |
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**Model & Development** |
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- **Developed by:** ruslanmv |
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- **License:** Apache-2.0 |
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- **Finetuned from model:** meta-llama/Meta-Llama-3-8B-Instruct |
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**Key Features** |
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- **Medical Focus:** Optimized to address health-related inquiries. |
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- **Knowledge Base:** Trained on a comprehensive medical dataset. |
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- **Text Generation:** Generates informative and potentially helpful responses. |
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**Installation** |
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This model is accessible through the Hugging Face Transformers library. Install it using pip: |
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```bash |
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!python -m pip install --upgrade pip |
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!pip3 install torch==2.2.1 torchvision torchaudio xformers --index-url https://download.pytorch.org/whl/cu121 |
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!pip install bitsandbytes accelerate |
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``` |
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**Usage Example** |
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Here's a Python code snippet demonstrating how to interact with the `ai-medical-model-32bit` model and generate answers to your medical questions: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig |
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import torch |
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model_name = "ruslanmv/ai-medical-model-32bit" |
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device_map = 'auto' |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_dtype=torch.float16, |
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) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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quantization_config=bnb_config, |
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trust_remote_code=True, |
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use_cache=False, |
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device_map=device_map |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) |
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tokenizer.pad_token = tokenizer.eos_token |
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def askme(question): |
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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|>" |
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# Tokenizing the input and generating the output |
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#prompt = f"{question}" |
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# Tokenizing the input and generating the output |
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inputs = tokenizer([prompt], return_tensors="pt").to("cuda") |
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outputs = model.generate(**inputs, max_new_tokens=256, use_cache=True) |
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answer = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] |
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# Try Remove the prompt |
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try: |
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# Split the answer at the first line break, assuming system intro and question are on separate lines |
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answer_parts = answer.split("\n", 1) |
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# If there are multiple parts, consider the second part as the answer |
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if len(answer_parts) > 1: |
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answers = answer_parts[1].strip() # Remove leading/trailing whitespaces |
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else: |
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answers = "" # If no split possible, set answer to empty string |
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print(f"Answer: {answers}") |
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except: |
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print(answer) |
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# Example usage |
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# - Question: Make the question. |
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question="What was the main cause of the inflammatory CD4+ T cells?" |
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askme(question) |
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``` |
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the type of answer is : |
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``` |
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Answer: I'm happy to help! |
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The main cause of inflammatory CD4+ T cells is a complex process that involves multiple factors. However, some of the key triggers include: |
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1. Activation of CD4+ T cells: CD4+ T cells are activated by antigens, cytokines, and other signals, leading to their proliferation and differentiation into effector cells. |
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2. Cytokine production: Activated CD4+ T cells produce cytokines such as interleukin-2 (IL-2), interferon-gamma (IFN-γ), and tumor necrosis factor-alpha (TNF-α), which promote inflammation and immune responses. |
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3. Chemokine production: CD4+ T cells also produce chemokines, such as CCL3, CCL4, and CCL5, which attract other immune cells to the site of inflammation. |
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4. Toll-like receptor (TLR) activation: TLRs are pattern recognition receptors that recognize pathogen-associated molecular patterns (PAMPs) and activate CD4+ T cells. |
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5. Bacterial or viral infections: Infections caused by bacteria, viruses, or fungi can trigger the activation of CD4+ T cells and the production of cytokines and chemokines |
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``` |
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**Important Note** |
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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. |
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**License** |
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This model is distributed under the Apache License 2.0 (see LICENSE file for details). |
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**Contributing** |
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We welcome contributions to this repository! If you have improvements or suggestions, feel free to create a pull request. |
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**Disclaimer** |
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While we strive to provide informative responses, the accuracy of the model's outputs cannot be guaranteed. |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ruslanmv__ai-medical-model-32bit) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |67.67| |
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|AI2 Reasoning Challenge (25-Shot)|61.43| |
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|HellaSwag (10-Shot) |78.69| |
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|MMLU (5-Shot) |68.10| |
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|TruthfulQA (0-shot) |51.99| |
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|Winogrande (5-shot) |75.77| |
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|GSM8k (5-shot) |70.05| |
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