Instructions to use beowolx/CodeNinja-1.0-OpenChat-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use beowolx/CodeNinja-1.0-OpenChat-7B with Transformers:

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

pipe = pipeline("text-generation", model="beowolx/CodeNinja-1.0-OpenChat-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("beowolx/CodeNinja-1.0-OpenChat-7B")
model = AutoModelForCausalLM.from_pretrained("beowolx/CodeNinja-1.0-OpenChat-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use beowolx/CodeNinja-1.0-OpenChat-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "beowolx/CodeNinja-1.0-OpenChat-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beowolx/CodeNinja-1.0-OpenChat-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/beowolx/CodeNinja-1.0-OpenChat-7B

SGLang

How to use beowolx/CodeNinja-1.0-OpenChat-7B 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 "beowolx/CodeNinja-1.0-OpenChat-7B" \
    --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": "beowolx/CodeNinja-1.0-OpenChat-7B",
		"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 "beowolx/CodeNinja-1.0-OpenChat-7B" \
        --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": "beowolx/CodeNinja-1.0-OpenChat-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use beowolx/CodeNinja-1.0-OpenChat-7B with Docker Model Runner:
```
docker model run hf.co/beowolx/CodeNinja-1.0-OpenChat-7B
```

CodeNinja-1.0-OpenChat-7B / README.md

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	---
	license: mit
	datasets:
	- glaiveai/glaive-code-assistant-v2
	- TokenBender/code_instructions_122k_alpaca_style
	language:
	- en
	metrics:
	- code_eval
	pipeline_tag: text-generation
	tags:
	- code
	- text-generation-inference
	---

	<p align="center">
	<img width="700px" alt="DeepSeek Coder" src="https://cdn-uploads.huggingface.co/production/uploads/64b566ab04fa6584c03b5247/5COagfF6EwrV4utZJ-ClI.png">
	</p>
	<hr>

	# CodeNinja: Your Advanced Coding Assistant

	## Overview

	CodeNinja is an enhanced version of the renowned model [openchat/openchat-3.5-1210](https://huggingface.co/openchat/openchat-3.5-1210). It having been fine-tuned through Supervised Fine Tuning on two expansive datasets, encompassing over 400,000 coding instructions. Designed to be an indispensable tool for coders, CodeNinja aims to integrate seamlessly into your daily coding routine.

	Discover the quantized versions at: [beowolx/CodeNinja-1.0-OpenChat-7B-GGUF](https://huggingface.co/beowolx/CodeNinja-1.0-OpenChat-7B-GGUF).

	### Key Features

	- Expansive Training Database: CodeNinja has been refined with datasets from [glaiveai/glaive-code-assistant-v2](https://huggingface.co/datasets/glaiveai/glaive-code-assistant-v2) and [TokenBender/code_instructions_122k_alpaca_style](https://huggingface.co/datasets/TokenBender/code_instructions_122k_alpaca_style), incorporating around 400,000 coding instructions across various languages including Python, C, C++, Rust, Java, JavaScript, and more.

	- Flexibility and Scalability: Available in a 7B model size, CodeNinja is adaptable for local runtime environments.

	- Advanced Code Completion: With a substantial context window size of 8192, it supports comprehensive project-level code completion.

	## Prompt Format

	CodeNinja maintains the same prompt structure as OpenChat 3.5. Effective utilization requires adherence to this format:

	```
	GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant: Hi<\|end_of_turn\|>GPT4 Correct User: How are you today?<\|end_of_turn\|>GPT4 Correct Assistant:
	```

	🚨 Important: Ensure the use of `<\|end_of_turn\|>` as the end-of-generation token.

	Adhering to this format is crucial for optimal results.

	## Usage Instructions

	### Using LM Studio

	The simplest way to engage with CodeNinja is via the [quantized versions](https://huggingface.co/beowolx/CodeNinja-1.0-OpenChat-7B-GGUF) on [LM Studio](https://lmstudio.ai/). Ensure you select the "OpenChat" preset, which incorporates the necessary prompt format. The preset is also available in this [gist](https://gist.github.com/beowolx/b219466681c02ff67baf8f313a3ad817).

	### Using the Transformers Library

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch

	# Initialize the model
	model_path = "beowolx/CodeNinja-1.0-OpenChat-7B"
	model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto")
	# Load the OpenChat tokenizer
	tokenizer = AutoTokenizer.from_pretrained("openchat/openchat-3.5-1210", use_fast=True)

	def generate_one_completion(prompt: str):
	messages = [
	{"role": "user", "content": prompt},
	{"role": "assistant", "content": ""} # Model response placeholder
	]

	# Generate token IDs using the chat template
	input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True)

	# Produce completion
	generate_ids = model.generate(
	torch.tensor([input_ids]).to("cuda"),
	max_length=256,
	pad_token_id=tokenizer.pad_token_id,
	eos_token_id=tokenizer.eos_token_id
	)

	# Process the completion
	completion = tokenizer.decode(generate_ids[0], skip_special_tokens=True)
	completion = completion.split("\n\n\n")[0].strip()

	return completion
	```

	## License
	CodeNinja is licensed under the MIT License, with model usage subject to the Model License.

	## Contact
	For queries or support, please open an issue in the repository.