Update README.md with model details and usage instructions

README.md

@@ -1,3 +1,104 @@
 ---
-license: mit
+license: apache-2.0
+base_model: meta-llama/Meta-Llama-3-8B
+tags:
+- trialpanorama
+- clinical-trials
+- sample-size-estimation
+- rlvr
+- reinforcement-learning
+- llama-3
+language:
+- en
+pipeline_tag: text-generation
 ---
+
+# LLaMA-3-8B-TP
+
+This model is fine-tuned from [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) by using [TrialPanorama dataset](https://huggingface.co/datasets/TrialPanorama/Dataset) for clinical trials.
+
+## Model Details
+
+- **Base Model**: Meta-Llama-3-8B-Instruct
+- **Fine-tuning Method**: Two-stage training
+  - Stage 1: Supervised Fine-Tuning (SFT) for knowledge injection
+  - Stage 2: RLVR (Reinforcement Learning with Verifiable Reward)
+
+## Usage
+
+### Basic Usage with Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load model and tokenizer
+model_name = "TrialPanorama/LLaMA-3-8B-TP"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Prepare input (a toy example)
+prompt = """Given the following clinical trial information, estimate the required sample size:
+
+[Input Information]
+
+Please provide the estimated sample size and reasoning."""
+
+# Generate response
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    temperature=0.6,
+    top_p=0.95,
+    do_sample=True
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### Usage with vLLM (Recommended for Production)
+
+```python
+from vllm import LLM, SamplingParams
+
+# Initialize vLLM
+llm = LLM(
+    model="TrialPanorama/LLaMA-3-8B-TP",
+    tensor_parallel_size=1,
+    dtype="bfloat16"
+)
+
+# Set sampling parameters
+sampling_params = SamplingParams(
+    temperature=0.6,
+    top_p=0.95,
+    max_tokens=512
+)
+
+# Generate
+prompts = ["Your sample size estimation prompt here"]
+outputs = llm.generate(prompts, sampling_params)
+
+for output in outputs:
+    print(output.outputs[0].text)
+```
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@article{wang2025trialpanorama,
+  title     = {Developing Large Language Models for Clinical Research Using One Million Clinical Trials},
+  author    = {Wang, Zifeng and Lin, Jiacheng and Jin, Qiao and Gao, Junyi and Pradeepkumar, Jathurshan and Jiang, Pengcheng and Lu, Zhiyong and Sun, Jimeng},
+  journal   = {arXiv preprint arXiv:2505.16097},
+  year      = {2025},
+  url       = {https://arxiv.org/abs/2505.16097}
+}
+```