Instructions to use kavinduc/devops-mastermind with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kavinduc/devops-mastermind with Transformers:

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

pipe = pipeline("text-generation", model="kavinduc/devops-mastermind")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kavinduc/devops-mastermind")
model = AutoModelForCausalLM.from_pretrained("kavinduc/devops-mastermind", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kavinduc/devops-mastermind with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kavinduc/devops-mastermind"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kavinduc/devops-mastermind",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kavinduc/devops-mastermind

SGLang

How to use kavinduc/devops-mastermind 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 "kavinduc/devops-mastermind" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kavinduc/devops-mastermind",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "kavinduc/devops-mastermind" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kavinduc/devops-mastermind",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kavinduc/devops-mastermind with Docker Model Runner:
```
docker model run hf.co/kavinduc/devops-mastermind
```

devops-mastermind / README.md

kavinduc

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	---
	license: mit
	datasets:
	- adeeshajayasinghe/devops-guide-demo
	metrics:
	- accuracy
	base_model:
	- microsoft/phi-2
	new_version: microsoft/phi-2
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- code
	- text-generation-inference
	---

	# DevOps Mastermind Model

	This repository hosts the DevOps Mastermind model, a pre-trained model based on `microsoft/phi-2` with modifications tailored for specialized DevOps knowledge tasks. The model is designed to support various downstream tasks, such as code generation, documentation assistance, and knowledge inference in DevOps domains.

	## Model Details

	- Base Model: `microsoft/phi-2`
	- Purpose: Enhanced with additional training and modifications for DevOps and software engineering contexts.
	- Files Included:
	- `config.json`: Model configuration.
	- `pytorch_model.bin`: The primary model file containing weights.
	- `tokenizer.json`: Tokenizer for processing text inputs.
	- `added_tokens.json`: Additional tokens specific to DevOps vocabulary.
	- `generation_config.json`: Generation configuration for text generation tasks.
	- Other auxiliary files required for model usage and compatibility.

	## Usage

	To load and use this model in your code, run the following commands:

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	# Load the model and tokenizer
	model_name = "kavinduc/devops-mastermind"
	model = AutoModelForCausalLM.from_pretrained(model_name)
	tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)

	# Example usage
	input_text = "Explain how to set up a CI/CD pipeline"
	inputs = tokenizer(input_text, return_tensors="pt")
	outputs = model.generate(**inputs)
	generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
	print(generated_text)