Instructions to use dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf",
	filename="llama-3-8B-sqlcorder-chinese-instruct-fp16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf
# Run inference directly in the terminal:
llama-cli -hf dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf
# Run inference directly in the terminal:
llama-cli -hf dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf

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 dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf
# Run inference directly in the terminal:
./llama-cli -hf dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf

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 dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf

Use Docker

docker model run hf.co/dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf

LM Studio
Jan
Ollama
How to use dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf with Ollama:
```
ollama run hf.co/dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf
```

Unsloth Studio new

How to use dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf 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 dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf 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 dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf to start chatting

Docker Model Runner
How to use dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf with Docker Model Runner:
```
docker model run hf.co/dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf
```

Lemonade

How to use dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dusensen/llama-3-8B-sqlcorder-chinese-Instruct-gguf

Run and chat with the model

lemonade run user.llama-3-8B-sqlcorder-chinese-Instruct-gguf-{{QUANT_TAG}}

List all available models

lemonade list

Model Description

Developed by: SenSen

增加模型对于中文语义理解增加中文文本转sql数据集

项目简介

微调模型: [llama-3-8b-sqlcorder] 项目地址：(https://github.com/dusens/llama-3-8B-Instruct-text2sql)

理想的推理和提示参数

Set temperature to 0, and do not do sampling.

Prompt

<|begin_of_text|><|start_header_id|>user<|end_header_id|>

Generate a SQL query to answer this question: `{user_question}`
{instructions}

DDL statements:
{create_table_statements}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

The following SQL query best answers the question `{user_question}`:
```sql

Downloads last month: 14

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

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