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
```

Fine tune with these datasets (ise-uiuc/Magicoder-OSS-Instruct-75K ise-uiuc/Magicoder-Evol-Instruct-110K)

by NK-Spacewalk - opened Jan 1, 2024

Discussion

NK-Spacewalk

Jan 1, 2024

CodeNinja-1.0-OpenChat-7B is very good model, if possible please try fine tune it with following datasets. I think , it can improve the model performance.

ise-uiuc/Magicoder-OSS-Instruct-75K
ise-uiuc/Magicoder-Evol-Instruct-110K

beowolx

Owner Jan 1, 2024

hey thanks for the feedback, i'm planning to release a new version soon with a dataset created by me, stay tuned 😉

Manaconda

Jan 3, 2024

This model performs well, would love to see some updates/training based on the latest C# coding models.

NK-Spacewalk

Jan 4, 2024

hey thanks for the feedback, i'm planning to release a new version soon with a dataset created by me, stay tuned 😉

waiting :)

NK-Spacewalk

Jan 5, 2024

small request, recently, Julia language 1.10 version released and brings new features. most of llama models trained data on julia 1.7 version, 1.8 version.
Please include this latest version of julia in your datasets, "https://raw.githubusercontent.com/JuliaLang/docs.julialang.org/assets/julia-1.10.0.pdf" , and https://docs.julialang.org/en/v1/, https://github.com/JuliaLang/julia.

due to this reason, no llama , deepseek models performs best for julia code generations.
Thank you. @beowolx

supercharge19

Jan 7, 2024

It would really be nice to have Julia included along with dashboards, web apps and animation and complex math solving capabilities or deep learning models creating capabilities.

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