初始化项目，由ModelHub XC社区提供模型

Model: FreedomIntelligence/HuatuoGPT-3-7B-Pangu
Source: Original Platform

README.md

@@ -0,0 +1,130 @@
+---
+library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
+language:
+- en
+- zh
+base_model:
+- FreedomIntelligence/openPangu-Embedded-7B
+tags:
+- medical
+- reasoning
+---
+
+<div align="center">
+  <h1>🩺 HuatuoGPT-3-7B-Pangu</h1>
+</div>
+
+<div align="center">
+  <a href="https://github.com/FreedomIntelligence/HuatuoGPT-3" target="_blank">🏠 GitHub</a>
+  |
+  <a href="" target="_blank">📄 Paper</a>
+</div>
+
+# <span>Introduction</span>
+
+**HuatuoGPT-3** is an open-source medical LLM trained with **SeedRL**, an RL-only domain adaptation paradigm that transforms a base model into a medical expert in a single RL stage.
+
+**HuatuoGPT-3-7B-Pangu** is the Pangu-based variant in the HuatuoGPT-3 series. Different from the Qwen-based versions, it is built on **FreedomIntelligence/openPangu-Embedded-7B** and trained with **Ascend NPUs**.
+
+For more information, visit our GitHub repository:
+[https://github.com/FreedomIntelligence/HuatuoGPT-3](https://github.com/FreedomIntelligence/HuatuoGPT-3)
+
+> [!IMPORTANT]
+> **HuatuoGPT-3-7B-Pangu is set to thinking mode by default.** Since it is based on openPangu, the generated reasoning content is placed between `[unused16]` and `[unused17]`, and the final response starts after `[unused17]`.
+
+# <span>Model Info</span>
+
+| Model | Description | Backbone | Link |
+| --- | --- | --- | --- |
+| **HuatuoGPT-3-32B** | 32B medical LLM trained with SeedRL | Qwen3-32B | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-3-32B) |
+| **HuatuoGPT-3-8B** | 8B medical LLM trained with SeedRL | Qwen3-8B-Base | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-3-8B) |
+| **HuatuoGPT-3-7B-Pangu** | 7B medical LLM trained with SeedRL | openPangu-Embedded-7B | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-3-7B-Pangu) |
+
+# <span>Usage</span>
+
+You can use HuatuoGPT-3-7B-Pangu in the same way as `FreedomIntelligence/openPangu-Embedded-7B`. 
+
+When using this model, please note that the Pangu backbone requires `trust_remote_code=True` in Transformers, and `--trust_remote_code` when serving with vLLM.
+
+- Direct inference:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "FreedomIntelligence/HuatuoGPT-3-7B-Pangu"
+
+tokenizer = AutoTokenizer.from_pretrained(
+    model_name,
+    use_fast=False,
+    trust_remote_code=True
+)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    trust_remote_code=True,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+messages = [
+    {"role": "user", "content": "What are the common causes of chest pain?"}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=4096,
+    eos_token_id=45892,
+    return_dict_in_generate=True
+)
+
+input_length = inputs.input_ids.shape[1]
+generated_tokens = outputs.sequences[:, input_length:]
+output_text = tokenizer.decode(generated_tokens[0])
+
+thinking_content = output_text.split("[unused17]")[0].split("[unused16]")[-1].strip()
+content = output_text.split("[unused17]")[-1].split("[unused10]")[0].strip()
+
+print("thinking content:", thinking_content)
+print("content:", content)
+```
+
+- You can also serve the model with vLLM. Make sure to include `--trust_remote_code`:
+
+```bash
+CUDA_VISIBLE_DEVICES=0 \
+vllm serve FreedomIntelligence/HuatuoGPT-3-7B-Pangu \
+  --served-model-name HuatuoGPT-3-7B-Pangu \
+  --trust_remote_code \
+  --port 8000
+```
+
+Or:
+
+```bash
+CUDA_VISIBLE_DEVICES=0 \
+python -m vllm.entrypoints.openai.api_server \
+  --model FreedomIntelligence/HuatuoGPT-3-7B-Pangu \
+  --served-model-name HuatuoGPT-3-7B-Pangu \
+  --trust_remote_code \
+  --port 8000
+```
+
+
+# <span>📖 Citation</span>
+
+```bibtex
+@article{huatuogpt3,
+  title={HuatuoGPT-3: RL-Only Domain Adaptation from Base Models via Off-Policy Seeding},
+  author={Coming soon},
+  journal={arXiv preprint},
+  year={2026}
+}
+```