Instructions to use RomilY/PCaPLMM_SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RomilY/PCaPLMM_SFT with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="RomilY/PCaPLMM_SFT", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("RomilY/PCaPLMM_SFT", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RomilY/PCaPLMM_SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RomilY/PCaPLMM_SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RomilY/PCaPLMM_SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RomilY/PCaPLMM_SFT

SGLang

How to use RomilY/PCaPLMM_SFT with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "RomilY/PCaPLMM_SFT" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RomilY/PCaPLMM_SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "RomilY/PCaPLMM_SFT" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RomilY/PCaPLMM_SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RomilY/PCaPLMM_SFT with Docker Model Runner:
```
docker model run hf.co/RomilY/PCaPLMM_SFT
```

RomilY commited on Dec 11, 2025

Commit

39fae4b

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1 Parent(s): beff36d

Update README.md

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@@ -20,7 +20,7 @@ PCaPLMM_SFT 是一个基于 Baichuan2-7B-chat 应用 LoRA 微调经 LLaMA-Factor
 - 微调方式：LoRA (rank=8, 使用 bf16)
 - 训练架构：LLaMA-Factory (v0.7.0)
 - 训练数据：基于系统纳入的2211篇文献构建了面向前列腺癌生活方式场景的训练数据集，包括营养管理、体力活动、体重管控、药物从实性、心理支持等场景
-表2.MedLIFE-Pca-Train数据集的数据构成
 |数据集名称     |类型数据量  |                                   描述                                                    |
 |预训练数据集   |文本数据    |2211 篇文献	包含 1516 篇原创性研究文章和695篇与前列腺癌生活方式相关的综述，用于领域继续预训练        |
 |单轮对话数据集 |医疗对话    |42,670 组	基于知识库生成的单轮患者问答，覆盖饮食营养、体力活动、体重管理、心理支持、药物依从等主题 |
@@ -31,7 +31,7 @@ PCaPLMM_SFT 是一个基于 Baichuan2-7B-chat 应用 LoRA 微调经 LLaMA-Factor
-> 注：根据随机抽样100条问答对进行人工评分
 ## 📚 训练数据生成流程

 - 微调方式：LoRA (rank=8, 使用 bf16)
 - 训练架构：LLaMA-Factory (v0.7.0)
 - 训练数据：基于系统纳入的2211篇文献构建了面向前列腺癌生活方式场景的训练数据集，包括营养管理、体力活动、体重管控、药物从实性、心理支持等场景
+表 MedLIFE-Pca-Train数据集的数据构成
 |数据集名称     |类型数据量  |                                   描述                                                    |
 |预训练数据集   |文本数据    |2211 篇文献	包含 1516 篇原创性研究文章和695篇与前列腺癌生活方式相关的综述，用于领域继续预训练        |
 |单轮对话数据集 |医疗对话    |42,670 组	基于知识库生成的单轮患者问答，覆盖饮食营养、体力活动、体重管理、心理支持、药物依从等主题 |
+> 注：根据随机抽样50条问答对进行人工评分
 ## 📚 训练数据生成流程