license: other
library_name: transformers
tags:
- generated_from_trainer
- medical
- Healthcare & Lifesciences
- BioMed
- chain-of-thought
base_model: qwen/Qwen2.5-3b-Instruct
thumbnail: https://collaiborate.com/logo/logo-blue-bg-1.png
model-index:
- name: Bio-Medical-3B-CoT-012025
results: []
datasets:
- collaiborateorg/BioMedData
Bio-Medical-3B-CoT-012025
This model is a fine-tuned version of Qwen2.5-3b-Instruct on our custom "BioMedData" dataset, enhanced with chain-of-thought prompting instructions to introduce advanced reasoning capabilities. It has been specifically optimized for applications in the Healthcare & Lifesciences (HLS) domain.
Model details
Model Name: Bio-Medical-3B-CoT-012025
Base Model: Qwen2.5-3b-Instruct
Parameter Count: 3 billion
Training Data: Custom high-quality biomedical dataset with chain-of-thought examples.
Number of Entries in Dataset: 600,000+
Dataset Composition: The dataset comprises both synthetic and manually curated samples, ensuring diverse and comprehensive coverage of biomedical knowledge.
Model description
The Bio-Medical-3B-CoT-012025 model is designed to provide accurate, context-aware, and reasoning-driven text generation in the biomedical domain. It has been fine-tuned on a dataset that includes chain-of-thought prompting to enable logical reasoning and better interpretability of its outputs.
This model is tailored for:
- Understanding and generating domain-specific content in the healthcare and biomedical fields.
- Answering complex questions that require step-by-step reasoning.
- Supporting professionals, researchers, and students in clinical and scientific tasks.
Evaluation Metrics
Bio-Medical-3B-CoT-012025 has been evaluated using the Eleuther AI Language Model Evaluation Harness framework on the following tasks:
- medmcqa
- medqa_4options
- mmlu_anatomy
- mmlu_clinical_knowledge
- mmlu_college_biology
- mmlu_college_medicine
- mmlu_medical_genetics
- mmlu_professional_medicine
- pubmedqa
Results show consistent performance improvements over general-purpose models of similiar size, particularly in tasks requiring reasoning.
Intended uses & limitations
Intended Uses:
- Research Support: Assisting researchers in extracting and generating insights from biomedical texts.
- Clinical Decision Support: Aiding in the interpretation of clinical data and evidence-based recommendations.
- Educational Tool: Enabling students and professionals to understand complex biomedical concepts.
Limitations and Ethical Considerations:
- Biases: The model may reflect biases present in its training data. While efforts were made to mitigate biases, some may persist.
- Accuracy: The model's responses should be validated against reliable sources, especially in critical or clinical contexts.
- Ethical Use: The model is intended to complement, not replace, expert judgment. It should be deployed responsibly in high-stakes environments.
How to use
import transformers
import torch
model_id = "ContactDoctor/Bio-Medical-3B-CoT-012025"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are an expert trained on healthcare and biomedical domain!"},
{"role": "user", "content": "What are the potential causes of chronic fatigue in a 40-year-old male?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
License
This model is licensed under the Bio-Medical-3B-CoT-012025 (Non-Commercial Use Only). Please review the terms and conditions before using the model.
Contact Information
For further information, inquiries, or issues related to Bio-Medical-3B-CoT-012025, please contact:
Email: [email protected]
Website: https://www.contactdoctor.in
Training hyperparameters
The following hyperparameters were used during training:
- Learning Rate: 0.0002
- Train Batch Size: 12
- Eval Batch Size: 8
- Seed: 42
- Gradient Accumulation Steps: 4
- Total Train Batch Size: 32
- Optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
- LR Scheduler Type: Cosine
- LR Scheduler Warmup Ratio: 0.03
- Training Steps: 2000
- Mixed Precision Training: Native AMP
Framework versions
- PEFT: 0.11.0
- Transformers: 4.40.2
- Pytorch: 2.1.2
- Datasets: 2.19.1
- Tokenizers: 0.19.1
Citation
If you use Bio-Medical-3B-CoT-012025 in your research or applications, please cite it as follows:
@misc{ContactDoctor_Bio-Medical-3B-CoT-012025,
author = {ContactDoctor},
title = {Bio-Medical-3B-CoT-012025: A High-Performance Biomedical Language Model with Reasoning Capabilities},
year = {2025},
howpublished = {https://huggingface.co/ContactDoctor/Bio-Medical-3B-CoT-012025},
}