Instructions to use DrChamyoung/Xieral-Code-Gen-3B-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DrChamyoung/Xieral-Code-Gen-3B-Model with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DrChamyoung/Xieral-Code-Gen-3B-Model",
	filename="Xieral-Code-Gen-3B-Model.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 DrChamyoung/Xieral-Code-Gen-3B-Model with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DrChamyoung/Xieral-Code-Gen-3B-Model
# Run inference directly in the terminal:
llama-cli -hf DrChamyoung/Xieral-Code-Gen-3B-Model

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DrChamyoung/Xieral-Code-Gen-3B-Model
# Run inference directly in the terminal:
llama-cli -hf DrChamyoung/Xieral-Code-Gen-3B-Model

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 DrChamyoung/Xieral-Code-Gen-3B-Model
# Run inference directly in the terminal:
./llama-cli -hf DrChamyoung/Xieral-Code-Gen-3B-Model

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 DrChamyoung/Xieral-Code-Gen-3B-Model
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DrChamyoung/Xieral-Code-Gen-3B-Model

Use Docker

docker model run hf.co/DrChamyoung/Xieral-Code-Gen-3B-Model

LM Studio
Jan
Ollama
How to use DrChamyoung/Xieral-Code-Gen-3B-Model with Ollama:
```
ollama run hf.co/DrChamyoung/Xieral-Code-Gen-3B-Model
```

Unsloth Studio new

How to use DrChamyoung/Xieral-Code-Gen-3B-Model 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 DrChamyoung/Xieral-Code-Gen-3B-Model 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 DrChamyoung/Xieral-Code-Gen-3B-Model to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DrChamyoung/Xieral-Code-Gen-3B-Model to start chatting

Docker Model Runner
How to use DrChamyoung/Xieral-Code-Gen-3B-Model with Docker Model Runner:
```
docker model run hf.co/DrChamyoung/Xieral-Code-Gen-3B-Model
```

Lemonade

How to use DrChamyoung/Xieral-Code-Gen-3B-Model with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DrChamyoung/Xieral-Code-Gen-3B-Model

Run and chat with the model

lemonade run user.Xieral-Code-Gen-3B-Model-{{QUANT_TAG}}

List all available models

lemonade list

Xieral Code Gen 3B

Xieral Code Gen 3B is a decoder-only language model with 2.7 billion parameters. Developed from the Xieral-Code-Gen-3b, this model is designed specifically for code generation and software engineering tasks.

Model Overview

Architecture: Decoder-only language model
Parameters: 2.7 billion
Training Data: Combination of publicly available and synthetic datasets
Optimization: Direct Preference Optimization (DPO)
Fine-tuning: General code/software engineering conversations, SQL query generation, and discussion

Performance

Xieral Code Gen 3B has demonstrated competitive performance compared to other models of similar size:

MultiPL-E Metrics: Evaluated across various programming languages using the BigCode Evaluation Harness.
MT Bench: Shows strong results on code-related tasks.

Usage

This model is well-suited for:

General code/software engineering conversations
SQL query generation and discussion

Requirements

To run Xieral Code Gen 3B locally, you will need:

VRAM: 8GB+ (Graphics card with sufficient VRAM)
Dependencies: Ensure you have the necessary libraries and environment set up to run the model.

Installation

To install the required dependencies, use:

pip install -r requirements.txt

Downloads last month: 4

GGUF

Model size

3B params

Architecture

stablelm

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

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