Instructions to use comfyuiblog/Nxcode-CQ-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use comfyuiblog/Nxcode-CQ-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="comfyuiblog/Nxcode-CQ-7B-GGUF",
	filename="nxcode.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use comfyuiblog/Nxcode-CQ-7B-GGUF 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 comfyuiblog/Nxcode-CQ-7B-GGUF
# Run inference directly in the terminal:
llama cli -hf comfyuiblog/Nxcode-CQ-7B-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf comfyuiblog/Nxcode-CQ-7B-GGUF
# Run inference directly in the terminal:
llama cli -hf comfyuiblog/Nxcode-CQ-7B-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 comfyuiblog/Nxcode-CQ-7B-GGUF
# Run inference directly in the terminal:
./llama-cli -hf comfyuiblog/Nxcode-CQ-7B-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 comfyuiblog/Nxcode-CQ-7B-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf comfyuiblog/Nxcode-CQ-7B-GGUF

Use Docker

docker model run hf.co/comfyuiblog/Nxcode-CQ-7B-GGUF

LM Studio
Jan
Ollama
How to use comfyuiblog/Nxcode-CQ-7B-GGUF with Ollama:
```
ollama run hf.co/comfyuiblog/Nxcode-CQ-7B-GGUF
```

Unsloth Studio

How to use comfyuiblog/Nxcode-CQ-7B-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 comfyuiblog/Nxcode-CQ-7B-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 comfyuiblog/Nxcode-CQ-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for comfyuiblog/Nxcode-CQ-7B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use comfyuiblog/Nxcode-CQ-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/comfyuiblog/Nxcode-CQ-7B-GGUF
```

Lemonade

How to use comfyuiblog/Nxcode-CQ-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull comfyuiblog/Nxcode-CQ-7B-GGUF

Run and chat with the model

lemonade run user.Nxcode-CQ-7B-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Introduction

Nxcode-CQ-7B-GGUF is the GGUF Model of Nxcode-CQ-7B-orpo is an Monolithic Preference Optimization without Reference Model fine-tune of Qwen/CodeQwen1.5-7B on 100k samples of high-quality ranking data.

Evalplus

EvalPlus	pass@1
HumanEval	86.6
HumanEval+	83.5
MBPP(v0.2.0)	82.3
MBPP+(v0.2.0)	70.4

We use a simple template to generate the solution for evalplus:

"Complete the following Python function:\n{prompt}"

Evalplus Leaderboard

Models	HumanEval	HumanEval+
GPT-4-Turbo (April 2024)	90.2	86.6
GPT-4 (May 2023)	88.4	81.17
GPT-4-Turbo (Nov 2023)	85.4	79.3
CodeQwen1.5-7B-Chat	83.5	78.7
claude-3-opus (Mar 2024)	82.9	76.8
DeepSeek-Coder-33B-instruct	81.1	75.0
WizardCoder-33B-V1.1	79.9	73.2
OpenCodeInterpreter-DS-33B	79.3	73.8
speechless-codellama-34B-v2.0	77.4	72
GPT-3.5-Turbo (Nov 2023)	76.8	70.7
Llama3-70B-instruct	76.2	70.7