Instructions to use linkyfan/DeepSeek-7B-HalfDocter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use linkyfan/DeepSeek-7B-HalfDocter with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="linkyfan/DeepSeek-7B-HalfDocter",
	filename="DeepSeek-7B-HalfDocter_Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "{\n    \"question\": \"What is my name?\",\n    \"context\": \"My name is Clara and I live in Berkeley.\"\n}"
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use linkyfan/DeepSeek-7B-HalfDocter with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf linkyfan/DeepSeek-7B-HalfDocter:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf linkyfan/DeepSeek-7B-HalfDocter:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf linkyfan/DeepSeek-7B-HalfDocter:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf linkyfan/DeepSeek-7B-HalfDocter:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf linkyfan/DeepSeek-7B-HalfDocter:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf linkyfan/DeepSeek-7B-HalfDocter:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf linkyfan/DeepSeek-7B-HalfDocter:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf linkyfan/DeepSeek-7B-HalfDocter:Q4_K_M

Use Docker

docker model run hf.co/linkyfan/DeepSeek-7B-HalfDocter:Q4_K_M

LM Studio
Jan
Ollama
How to use linkyfan/DeepSeek-7B-HalfDocter with Ollama:
```
ollama run hf.co/linkyfan/DeepSeek-7B-HalfDocter:Q4_K_M
```

Unsloth Studio

How to use linkyfan/DeepSeek-7B-HalfDocter with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for linkyfan/DeepSeek-7B-HalfDocter to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for linkyfan/DeepSeek-7B-HalfDocter to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for linkyfan/DeepSeek-7B-HalfDocter to start chatting

Atomic Chat new
Docker Model Runner
How to use linkyfan/DeepSeek-7B-HalfDocter with Docker Model Runner:
```
docker model run hf.co/linkyfan/DeepSeek-7B-HalfDocter:Q4_K_M
```

Lemonade

How to use linkyfan/DeepSeek-7B-HalfDocter with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull linkyfan/DeepSeek-7B-HalfDocter:Q4_K_M

Run and chat with the model

lemonade run user.DeepSeek-7B-HalfDocter-Q4_K_M

List all available models

lemonade list

模型描述

该模型是专为医学应用设计的大型语言模型。目前是测试版本，它使用自定义数据集从DeepSeek-R1-Distill-Qwen-7B模型进行了微调。

该模型经过训练，可以理解某些疾病的表面症状并给出诊断。

！目前只是少量数据的训练集，请不要太相信结果，谢谢

Downloads last month: 33

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for linkyfan/DeepSeek-7B-HalfDocter

Base model

deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

Quantized

(175)

this model

linkyfan
/

DeepSeek-7B-HalfDocter

Model tree for linkyfan/DeepSeek-7B-HalfDocter

Dataset used to train linkyfan/DeepSeek-7B-HalfDocter