Instructions to use ayertiam/phi3-nl2bash-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ayertiam/phi3-nl2bash-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("microsoft/phi-3-mini-128k-instruct")
model = PeftModel.from_pretrained(base_model, "ayertiam/phi3-nl2bash-lora")

Transformers

How to use ayertiam/phi3-nl2bash-lora with Transformers:

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

pipe = pipeline("text-generation", model="ayertiam/phi3-nl2bash-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ayertiam/phi3-nl2bash-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ayertiam/phi3-nl2bash-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ayertiam/phi3-nl2bash-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ayertiam/phi3-nl2bash-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ayertiam/phi3-nl2bash-lora

SGLang

How to use ayertiam/phi3-nl2bash-lora 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 "ayertiam/phi3-nl2bash-lora" \
    --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": "ayertiam/phi3-nl2bash-lora",
		"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 "ayertiam/phi3-nl2bash-lora" \
        --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": "ayertiam/phi3-nl2bash-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ayertiam/phi3-nl2bash-lora with Docker Model Runner:
```
docker model run hf.co/ayertiam/phi3-nl2bash-lora
```

phi3-nl2bash-lora

This repository contains LoRA adapter weights fine-tuned on the jiacheng-ye/nl2bash dataset to convert natural language instructions into Linux bash commands.

⚠️ This repository contains LoRA adapters only, not the base model.
You must load these adapters on top of
microsoft/phi-3-mini-128k-instruct.

Intended use

The model is trained to output only valid bash commands, with no explanations.

Example

Input:

List all .txt files recursively and count lines

Output:

find . -name "*.txt" | xargs wc

Training summary

Base model: microsoft/phi-3-mini-128k-instruct
Fine-tuning method: LoRA (PEFT)
Trainer: TRL SFTTrainer
Dataset: jiacheng-ye/nl2bash
Output format: Bash commands only

Loading example

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base_model = "microsoft/phi-3-mini-128k-instruct"
lora_model = "ayertiam/phi3-nl2bash-lora"

tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

model = PeftModel.from_pretrained(model, lora_model)
model.eval()

Notes

These adapters are model-specific and only compatible with microsoft/phi-3-mini-128k-instruct.
For Ollama or GGUF usage, the LoRA must be merged into the base model and converted before inference as done here https://huggingface.co/ayertiam/phi3-nl2bash-gguf

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