adeeshajayasinghe/devops-guide-demo
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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")How to use kavinduc/devops-mastermind with vLLM:
# 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
}'docker model run hf.co/kavinduc/devops-mastermind
How to use kavinduc/devops-mastermind with SGLang:
# 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
}'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
}'How to use kavinduc/devops-mastermind with Docker Model Runner:
docker model run hf.co/kavinduc/devops-mastermind
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.
microsoft/phi-2config.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.To load and use this model in your code, run the following commands:
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)
Base model
microsoft/phi-2
docker model run hf.co/kavinduc/devops-mastermind