Instructions to use quickstep3621/dippy-v2-1-15 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use quickstep3621/dippy-v2-1-15 with Transformers:

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

pipe = pipeline("text-generation", model="quickstep3621/dippy-v2-1-15")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("quickstep3621/dippy-v2-1-15")
model = AutoModelForImageTextToText.from_pretrained("quickstep3621/dippy-v2-1-15")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use quickstep3621/dippy-v2-1-15 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "quickstep3621/dippy-v2-1-15"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "quickstep3621/dippy-v2-1-15",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/quickstep3621/dippy-v2-1-15

SGLang

How to use quickstep3621/dippy-v2-1-15 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 "quickstep3621/dippy-v2-1-15" \
    --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": "quickstep3621/dippy-v2-1-15",
		"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 "quickstep3621/dippy-v2-1-15" \
        --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": "quickstep3621/dippy-v2-1-15",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use quickstep3621/dippy-v2-1-15 with Docker Model Runner:
```
docker model run hf.co/quickstep3621/dippy-v2-1-15
```

Bifröst-27B

Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance.

Model Details

Model Name: Bifröst-27B
Base Architecture: gemma3
Application: Enterprise Secure Code Generation
Release Date: 16-March-2025

Intended Use

Bifröst is designed explicitly for:

Generating secure, efficient, and high-quality code.
Supporting development tasks within regulated enterprise environments.
Enhancing productivity by automating routine coding tasks without compromising security.

Features

Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards.
Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions.
Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2).

Limitations

Bifröst should be used under human supervision to ensure code correctness and security compliance.
Model-generated code should undergo appropriate security and quality assurance checks before deployment.

Ethical Considerations

Users are encouraged to perform regular audits and compliance checks on generated outputs.
Enterprises should implement responsible AI practices to mitigate biases or unintended consequences.

Usage

Below are some quick-start instructions for using the model with the transformers library.

Installation

$ pip install git+https://github.com/huggingface/transformers@v4.49.0-Gemma-3

Running with the `pipeline` API

from transformers import pipeline
import torch

pipe = pipeline(
    "text-generation",
    model="OpenGenerativeAI/Bifrost-27B",
    device="cuda",
    torch_dtype=torch.bfloat16
)

messages = [{"role": "user", "content": "Generate a secure API key management system."}]
output = pipe(text=messages, max_new_tokens=200)
print(output[0]["generated_text"])

Terms of Use

This model is released under the Gemma license. Users must comply with Google's Gemma Terms of Use, including restrictions on redistribution, modification, and commercial use.

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Safetensors

Model size

27B params

Tensor type

BF16

Model tree for quickstep3621/dippy-v2-1-15

Base model

google/gemma-3-27b-pt

Finetuned

google/gemma-3-27b-it