初始化项目，由ModelHub XC社区提供模型

Model: TsinghuaC3I/Llama-3.1-8B-UltraMedical
Source: Original Platform

README.md

@@ -0,0 +1,74 @@
+---
+license: llama3
+datasets:
+- TsinghuaC3I/UltraMedical
+- TsinghuaC3I/UltraMedical-Preference
+language:
+- en
+base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
+---
+<div align="center">
+<h1>
+  UltraMedical: Building Specialized Generalists in Biomedicine.
+</h1>
+</div>
+
+<p align="center">
+  <a href="https://huggingface.co/datasets/TsinghuaC3I/UltraMedical">SFT Dataset</a> •
+  <a href="https://huggingface.co/datasets/TsinghuaC3I/UltraMedical-Preference">Pref Dataset</a> •
+  <a href="https://huggingface.co/collections/TsinghuaC3I/ultramedical-66d4076bad293ffc4bc41327">Collection</a> •
+  <a href="https://github.com/TsinghuaC3I/UltraMedical">GitHub Code</a> •
+  <a href="https://arxiv.org/abs/2406.03949">Paper</a>
+</p>
+
+Llama-3.1-8B-UltraMedical is an open-access large language model (LLM) specialized in biomedicine. Developed by the Tsinghua C3I Lab, this model aims to enhance medical examination access, literature comprehension, and clinical knowledge.
+
+Building on the foundation of Meta's Llama-3.1-8B, Llama-3.1-8B-UltraMedical is trained on our [UltraMedical](https://github.com/TsinghuaC3I/UltraMedical) collection with supervised fine-tuning (SFT), iterative preference learning (like DPO and KTO). The UltraMedical collection is a large-scale, high-quality dataset of biomedical instructions, comprising 410,000 synthetic and manually curated samples, along with more than 100,000 preference data.
+
+
+### Evaluations
+
+| Model | MultiMedQA | MMLU | GPQA | Alpaca Eval 2 (LC/WR) |
+| ----- | ---------- | ---- | ---- | ------------- |
+| Llama-3.1-8B-Instruct | 71.38 | 73.00  | 30.40  | 20.90/21.80 |
+| Llama-3.1-8B-UltraMedical | 76.82 | 72.96 | 34.82 |  41.22/48.20  |
+
+### Inference with vLLM
+
+```python
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+
+llm = LLM(model="TsinghuaC3I/Llama-3.1-8B-UltraMedical", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("TsinghuaC3I/Llama-3.1-8B-UltraMedical")
+sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=1024, stop=["<|eot_id|>"])
+
+messages = [
+    {"role": "user", "content": """The question format used in the above input examples。"""},
+]
+prompts = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+print(prompts[0])
+"""
+<|begin_of_text|><|start_header_id|>user<|end_header_id|>
+
+{question}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+"""
+
+outputs = llm.generate(prompts=prompts, sampling_params=sampling_params)
+print(outputs[0].outputs[0].text)
+```
+
+
+### Citation
+
+```
+@misc{zhang2024ultramedical,
+      title={UltraMedical: Building Specialized Generalists in Biomedicine}, 
+      author={Kaiyan Zhang and Sihang Zeng and Ermo Hua and Ning Ding and Zhang-Ren Chen and Zhiyuan Ma and Haoxin Li and Ganqu Cui and Biqing Qi and Xuekai Zhu and Xingtai Lv and Hu Jinfang and Zhiyuan Liu and Bowen Zhou},
+      year={2024},
+      eprint={2406.03949},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```