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Model: TrialPanorama/Qwen-3-8B-TP
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-8B
+tags:
+- trialpanorama
+- clinical-trials
+- study-search
+- rlvr
+- reinforcement-learning
+- qwen3
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# Qwen-3-8B-TP
+
+This model is fine-tuned from [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) by using [TrialPanorama dataset](https://huggingface.co/datasets/TrialPanorama/Dataset) for clinical trials.
+
+## Model Details
+
+- **Base Model**: Qwen/Qwen3-8B
+- **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/Qwen-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, search for relevant studies:
+
+[Input Information]
+
+Please provide relevant clinical trials and reasoning."""
+
+# Generate response
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    enable_thinking=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **model_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/Qwen-3-8B-TP",
+    tensor_parallel_size=1,
+    dtype="bfloat16",
+    enable_reasoning=True,
+    reasoning_parser="deepseek_r1"
+)
+
+# Set sampling parameters
+sampling_params = SamplingParams(
+    temperature=0.6,
+    top_p=0.95,
+    max_tokens=512
+)
+
+# Generate
+prompts = ["Your study search prompt here"]
+outputs = llm.generate(prompts, sampling_params)
+
+for output in outputs:
+    print(output.outputs[0].text)
+```
+
+### Usage with SGLang
+
+```python
+from sglang import Runtime, Engine, ModelConfig, SamplingParams
+
+# Initialize SGLang
+runtime = Runtime(
+    model_path="TrialPanorama/Qwen-3-8B-TP",
+    reasoning_parser="qwen3"
+)
+
+# Set sampling parameters
+sampling_params = SamplingParams(
+    temperature=0.6,
+    top_p=0.95,
+    max_new_tokens=512
+)
+
+# Generate
+prompts = ["Your study search prompt here"]
+outputs = runtime.generate(prompts, sampling_params)
+
+for output in outputs:
+    print(output.text)
+```
+
+## Citation
+
+```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}
+}
+```