Instructions to use ayertiam/phi3-nl2bash-canonical-17012026 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ayertiam/phi3-nl2bash-canonical-17012026 with Transformers:

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

pipe = pipeline("text-generation", model="ayertiam/phi3-nl2bash-canonical-17012026", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ayertiam/phi3-nl2bash-canonical-17012026", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("ayertiam/phi3-nl2bash-canonical-17012026", trust_remote_code=True)
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]:]))

llama-cpp-python

How to use ayertiam/phi3-nl2bash-canonical-17012026 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ayertiam/phi3-nl2bash-canonical-17012026",
	filename="gguf/fp16/phi3-nl2bash-canonical-17012026.fp16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ayertiam/phi3-nl2bash-canonical-17012026 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ayertiam/phi3-nl2bash-canonical-17012026:Q4_0
# Run inference directly in the terminal:
llama-cli -hf ayertiam/phi3-nl2bash-canonical-17012026:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ayertiam/phi3-nl2bash-canonical-17012026:Q4_0
# Run inference directly in the terminal:
llama-cli -hf ayertiam/phi3-nl2bash-canonical-17012026:Q4_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ayertiam/phi3-nl2bash-canonical-17012026:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf ayertiam/phi3-nl2bash-canonical-17012026:Q4_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ayertiam/phi3-nl2bash-canonical-17012026:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ayertiam/phi3-nl2bash-canonical-17012026:Q4_0

Use Docker

docker model run hf.co/ayertiam/phi3-nl2bash-canonical-17012026:Q4_0

LM Studio
Jan

vLLM

How to use ayertiam/phi3-nl2bash-canonical-17012026 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ayertiam/phi3-nl2bash-canonical-17012026"
# 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-canonical-17012026",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ayertiam/phi3-nl2bash-canonical-17012026:Q4_0

SGLang

How to use ayertiam/phi3-nl2bash-canonical-17012026 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-canonical-17012026" \
    --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-canonical-17012026",
		"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-canonical-17012026" \
        --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-canonical-17012026",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ayertiam/phi3-nl2bash-canonical-17012026 with Ollama:
```
ollama run hf.co/ayertiam/phi3-nl2bash-canonical-17012026:Q4_0
```

Unsloth Studio new

How to use ayertiam/phi3-nl2bash-canonical-17012026 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 ayertiam/phi3-nl2bash-canonical-17012026 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 ayertiam/phi3-nl2bash-canonical-17012026 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ayertiam/phi3-nl2bash-canonical-17012026 to start chatting

Docker Model Runner
How to use ayertiam/phi3-nl2bash-canonical-17012026 with Docker Model Runner:
```
docker model run hf.co/ayertiam/phi3-nl2bash-canonical-17012026:Q4_0
```

Lemonade

How to use ayertiam/phi3-nl2bash-canonical-17012026 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ayertiam/phi3-nl2bash-canonical-17012026:Q4_0

Run and chat with the model

lemonade run user.phi3-nl2bash-canonical-17012026-Q4_0

List all available models

lemonade list

TL;DR: A deterministic Phi-3 fine-tune that converts natural language into a single, canonical, POSIX-safe Bash command — no explanations, no pipelines.

phi3-nl2bash-canonical

Model Summary

phi3-nl2bash-canonical is a task-specialized small language model fine-tuned to translate natural-language instructions into minimal, valid Bash commands.
The model is intentionally constrained to produce single, canonical, POSIX-safe commands without explanations, pipelines, subshells, or side effects.

This model is designed for command-line education, tooling, and evaluation, not for general-purpose chat.

Base Model

microsoft/phi-3-mini-4k-instruct

Training Data

The model was fine-tuned using a curated subset of the NL2Bash dataset combined with synthetic examples generated from a manually verified command core.

Dataset characteristics:

Only local, single-command Bash instructions
No pipelines, redirections, subshells, SSH, rsync, or environment variables
Restricted command set (e.g., ls, cd, mkdir, touch, cp, mv, chmod, cat, head, tail, basename, dirname, wc)
Synthetic augmentation used to improve coverage while preserving canonical form

The goal was precision and determinism, not breadth.

Training Method

Parameter-efficient fine-tuning (LoRA)
Conservative hyperparameters to avoid catastrophic forgetting
Instruction format: ChatML-style (<|user|>, <|assistant|>, <|end|>)

Model Variants

This repository contains multiple formats:

FP16 GGUF (gguf/fp16/):
Canonical archival format for reproducibility and re-quantization
Q4_0 GGUF (gguf/q4_0/):
CPU-efficient quantized model suitable for local inference (e.g., Ollama, llama.cpp)

Usage

Ollama

Example Modelfile:

FROM ./phi3-nl2bash-canonical-17012026.q4_0.gguf

SYSTEM You output only valid bash commands.
SYSTEM No explanations or markdown.

TEMPLATE """<|user|>
{{ .Prompt }}
<|assistant|>
"""

PARAMETER stop "<|end|>"
PARAMETER temperature 0

Example

Prompt: create a file called a

Output: touch a

Intended Use

Teaching command-line basics
Evaluating NL→CLI translation
Safe, constrained automation

Out of Scope

Complex shell scripting
Remote execution
File discovery or destructive commands
General-purpose conversation

Limitations

This model intentionally trades expressiveness for safety and determinism.
It may refuse or oversimplify complex requests.

Ethics & Safety

The model was trained to avoid unsafe shell constructs and does not generate commands involving networking, privilege escalation, or destructive operations unless explicitly specified in the prompt.

Citation

If you use this model in academic work, please cite the accompanying repository.Developed as part of an academic thesis (2026).

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Safetensors

Model size

4B params

Tensor type

BF16