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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Emaoso/Tangshi 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 Emaoso/Tangshi
# Run inference directly in the terminal:
llama cli -hf Emaoso/Tangshi

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Emaoso/Tangshi
# Run inference directly in the terminal:
llama cli -hf Emaoso/Tangshi

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 Emaoso/Tangshi
# Run inference directly in the terminal:
./llama-cli -hf Emaoso/Tangshi

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 Emaoso/Tangshi
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Emaoso/Tangshi

Use Docker

docker model run hf.co/Emaoso/Tangshi

LM Studio
Jan

vLLM

How to use Emaoso/Tangshi with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Emaoso/Tangshi

Ollama
How to use Emaoso/Tangshi with Ollama:
```
ollama run hf.co/Emaoso/Tangshi
```

Unsloth Studio

How to use Emaoso/Tangshi 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 Emaoso/Tangshi 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 Emaoso/Tangshi to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Emaoso/Tangshi to start chatting

How to use Emaoso/Tangshi with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Emaoso/Tangshi

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Emaoso/Tangshi"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use Emaoso/Tangshi with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Emaoso/Tangshi

Run and chat with the model

lemonade run user.Tangshi-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Emaoso/Tangshi with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Emaoso/Tangshi

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Emaoso/Tangshi

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Emaoso/Tangshi with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Emaoso/Tangshi

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Emaoso/Tangshi" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Tangshi / README.md

Emaoso

Update README.md

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	---
	language:
	- zh
	license: apache-2.0
	base_model: Qwen/Qwen2.5-0.5B
	pipeline_tag: text-generation
	tags:
	- 唐诗
	- 古诗生成
	- chinese-poetry
	- qwen2.5
	---

	# Tangshi｜中文唐诗生成模型
	基于Qwen2.5-0.5B微调的古诗专用大模型，擅长自动生成五言/七言绝句、律诗，专为古典诗词创作优化。训练数据为57000首唐诗全参数。

	## 仓库信息
	Huggingface地址：`Emaoso/Tangshi`
	包含两类权重：
	1. `model.safetensors`：原生transformers权重，用于Python代码调用
	2. `model-ollama.gguf`：GGUF量化权重，用于Ollama本地部署
	附带：Ollama一键构建配置 Modelfile

	## 一、Python Transformers调用（推荐）
	### 1.安装依赖
	```bash
	pip install torch transformers


	====================================
	代码示例
	from transformers import AutoTokenizer, AutoModelForCausalLM

	model_name = "Emaoso/Tangshi"
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(model_name)

	# 写诗指令
	prompt = "写一首春日五言绝句"
	inputs = tokenizer(prompt, return_tensors="pt")
	outputs = model.generate(**inputs, max_new_tokens=80)
	result = tokenizer.decode(outputs[0], skip_special_tokens=True)

	# 清洗多余注释
	import re
	result = re.sub(r'（.\|〖.\|见卷.*','',result)
	print(result)

	======================================
	ollama 使用
	ollama create tangshi https://huggingface.co/Emaoso/Tangshi/resolve/main/Modelfile
	ollama run tangshi