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

Libraries

How to use duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use duyntnet/Nxcode-CQ-7B-orpo-imatrix-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 duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF: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 duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF: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 duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF:Q4_K_M

Use Docker

docker model run hf.co/duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF:Q4_K_M

SGLang

How to use duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF with Ollama:
```
ollama run hf.co/duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF with Docker Model Runner:
```
docker model run hf.co/duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF:Q4_K_M
```

Lemonade

How to use duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Nxcode-CQ-7B-orpo-imatrix-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Quantizations of https://huggingface.co/NTQAI/Nxcode-CQ-7B-orpo

Open source inference clients/UIs

Closed source inference clients/UIs

LM Studio
More will be added...

From original readme

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

Bigcode Leaderboard

09/05/2024

Top 1 average score.

Top 2 winrate.

Quickstart

Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents. You should upgrade the transformers if you receive an error when loading the tokenizer

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "NTQAI/Nxcode-CQ-7B-orpo",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("NTQAI/Nxcode-CQ-7B-orpo")

prompt = """Complete the following Python function:
from typing import List


def has_close_elements(numbers: List[float], threshold: float) -> bool:
    """ Check if in given list of numbers, are any two numbers closer to each other than
    given threshold.
    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)
    False
    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)
    True
    """
"""
messages = [
    {"role": "user", "content": prompt}
]

inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
res = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)

Downloads last month: 439

GGUF

Model size

7B params

Architecture

qwen2

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Paper for duyntnet/Nxcode-CQ-7B-orpo-imatrix-GGUF

ORPO: Monolithic Preference Optimization without Reference Model

Paper • 2403.07691 • Published Mar 12, 2024 • 75