Tierney
/

DeepSeek-R1-Medical-Doctor-LoRA

Safetensors

Chinese

Model card Files Files and versions

xet

Community

Tierney commited on Mar 5

Commit

bc3c686

verified ·

1 Parent(s): 34b73ed

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +41 -192

README.md CHANGED Viewed

@@ -1,207 +1,56 @@
----
-library_name: transformers
-tags:
-- unsloth
-license: apache-2.0
-datasets:
-- FreedomIntelligence/medical-o1-reasoning-SFT
-language:
-- zh
-base_model:
-- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

+# DeepSeek-R1-Distill-Qwen-14B LoRA Adapter
+## 📌 模型简介
+本 LoRA 适配器基于 **DeepSeek-R1-Distill-Qwen-14B** 进行微调，主要优化医学领域的问答和推理能力。
+- 🔹 **基座模型**: [deepseek-ai/DeepSeek-R1-Distill-Qwen-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B)
+- 🔹 **微调方法**: LoRA（使用 [Unsloth](https://github.com/unslothai/unsloth) 进行优化）
+- 🔹 **适用场景**: 医学文本问答、医学知识增强
+---
+## 📂 使用方法
+### 🔄 加载 LoRA 适配器
+要使用本 LoRA 适配器，你需要加载原始 DeepSeek-R1-14B 模型，并应用 LoRA 权重：
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+base_model = "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B"
+lora_model = "your-huggingface-username/DeepSeek-R1-Distill-Qwen-14B-lora-med"
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+model = AutoModelForCausalLM.from_pretrained(base_model, torch_dtype="auto", device_map="auto")
+model = PeftModel.from_pretrained(model, lora_model)
+```
+### 🚀 推理示例
+```python
+input_text = "请问阿司匹林的主要适应症是什么？"
+inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_length=200)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+---
+## 🏗️ 训练信息
+- **训练环境**: RTX 4090, CUDA 12.6, WSL Ubuntu
+- **训练框架**: `transformers` + `peft` + `unsloth`
+- **训练参数**:
+  - LoRA Rank: 16
+  - Alpha: 32
+  - Dropout: 0.05
+  - Max Seq Length: 4096
+---
+## 📜 许可证
+本 LoRA 适配器基于 **DeepSeek-R1-Distill-Qwen-14B**，请遵守其[官方许可证](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B)。
+---
+## 📞 联系方式
+如果你有任何问题或建议，可以在讨论区留言，或者联系我！