Instructions to use simonbutt/tofuTune-phi3-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use simonbutt/tofuTune-phi3-medium with Transformers:

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

pipe = pipeline("text-generation", model="simonbutt/tofuTune-phi3-medium")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("simonbutt/tofuTune-phi3-medium")
model = AutoModelForCausalLM.from_pretrained("simonbutt/tofuTune-phi3-medium")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use simonbutt/tofuTune-phi3-medium with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "simonbutt/tofuTune-phi3-medium"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "simonbutt/tofuTune-phi3-medium",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/simonbutt/tofuTune-phi3-medium

SGLang

How to use simonbutt/tofuTune-phi3-medium 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 "simonbutt/tofuTune-phi3-medium" \
    --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": "simonbutt/tofuTune-phi3-medium",
		"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 "simonbutt/tofuTune-phi3-medium" \
        --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": "simonbutt/tofuTune-phi3-medium",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use simonbutt/tofuTune-phi3-medium with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for simonbutt/tofuTune-phi3-medium to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for simonbutt/tofuTune-phi3-medium to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for simonbutt/tofuTune-phi3-medium to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="simonbutt/tofuTune-phi3-medium",
    max_seq_length=2048,
)

Docker Model Runner
How to use simonbutt/tofuTune-phi3-medium with Docker Model Runner:
```
docker model run hf.co/simonbutt/tofuTune-phi3-medium
```

TofuTune Phi-3 Medium

TofuTune is designed to take a lightweight agile ticket describing a cloud infrastructure task and output valid terraform/openTofu code which completes the ticket.

Limitations

Small initial dataset to POC the usecase.
Only trained for use with the Google Cloud provider.

Format

Prompt:

## Ticket: Active Directory Domain Setup in GCP
## User Story:  
As a DevOps engineer, I want to deploy an Active Directory domain in GCP to centralize domain management for enhanced security and efficiency.
## Details: The task involves configuring a Google Managed Microsoft Active Directory domain. This setup will use a specific domain name and operate within the 'us-central1' region, with a reserved IP range to ensure secure and isolated network operations.
## Acceptance CriteriaDomain Configuration:
- A Google Managed Active Directory domain named tfgen-{suffix}.org.com should be successfully created.
- Location and IP Reservation: The domain should be located in us-central1 and must utilize the reserved IP range 192.168.255.0/24 for its operations.

Response:

resource "google_active_directory_domain" "ad-domain" {
  domain_name       = "tfgen-${local.name_suffix}.org.com"
  locations         = ["us-central1"]
  reserved_ip_range = "192.168.255.0/24" 
}