Instructions to use burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf
# Run inference directly in the terminal:
llama cli -hf burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf
# Run inference directly in the terminal:
llama cli -hf burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf

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 burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf
# Run inference directly in the terminal:
./llama-cli -hf burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf

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 burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf

Use Docker

docker model run hf.co/burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf

LM Studio
Jan

vLLM

How to use burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf

Ollama
How to use burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf with Ollama:
```
ollama run hf.co/burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf
```
Unsloth Desktop

How to use burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf with Docker Model Runner:
```
docker model run hf.co/burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf
```

Lemonade

How to use burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf

Run and chat with the model

lemonade run user.qwen2.5-coder-0.5b-nl2bash-gguf-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

🚀 Qwen2.5-Coder-0.5B-Instruct - NL2Bash (GGUF Quantized)

A fine-tuned version of Qwen/Qwen2.5-Coder-0.5B-Instruct specialized for converting natural language instructions directly into executable Linux/Ubuntu Bash commands with zero conversational filler.

📊 Dual-Track Benchmark Results

🎓 Track 1: Academic NL2Bash Benchmark (EMNLP 2018 / Microsoft CodeXGLUE)

Metric	Base Model (`qwen2.5-coder:0.5b`)	Fine-Tuned Model (`bash-coder-assistant`)	Improvement
Exact Match (EM %)	`0.0%`	`44.0%`	+44.0%
Token F1 Score	`12.93`	`81.60`	+68.7 pts
BLEU-4 Score	`4.60`	`65.01`	+60.4 pts
Bash Syntax Validity	`88.0%`	`100.0%`	+12.0%
Zero-Chatter Clean Format	`4.0%`	`100.0%`	+96.0%
Mean Latency (CPU)	`19.03s`	`1.58s`	⚡ 91.7% Faster

🛠️ Track 2: Real-World DevOps, Cloud & SysAdmin Benchmark

Metric	Base Model (`qwen2.5-coder:0.5b`)	Fine-Tuned Model (`bash-coder-assistant`)	Improvement
Exact Match (EM %)	`0.0%`	`15.0%`	+15.0%
Token F1 Score	`6.76`	`62.42`	+55.7 pts
Bash Syntax Validity	`90.0%`	`100.0%`	+10.0%
Mean Latency (CPU)	`20.98s`	`1.78s`	⚡ 91.5% Faster

🦙 Quickstart with Ollama

Download the qwen2.5-coder-0.5b-bash-gguf.gguf file and Modelfile.
Create the model in Ollama:

ollama create bash-coder-assistant -f Modelfile

Run a query:

ollama run bash-coder-assistant "Find all processes running on port 8080 and terminate them immediately"
# Output: sudo kill -9 $(lsof -i :8080 | awk '{print $2}')

🛠️ Training Details

Architecture: Qwen2.5-Coder (0.5B parameters)
Framework: Unsloth + TRL SFTTrainer
Dataset: emirkaanozdemr/bash_command_data_6K
LoRA Rank: r=16, lora_alpha=16
Training Compute: Google Colab Tesla T4 GPU (< 2GB VRAM, ~8 min runtime)
Final Loss: 0.6726 (converged from initial 4.1045)

Downloads last month: 56

GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-Coder-0.5B

Finetuned

Qwen/Qwen2.5-Coder-0.5B-Instruct

Quantized

(93)

this model

burhan2753y
/

qwen2.5-coder-0.5b-nl2bash-gguf

🚀 Qwen2.5-Coder-0.5B-Instruct - NL2Bash (GGUF Quantized)

📊 Dual-Track Benchmark Results

🎓 Track 1: Academic NL2Bash Benchmark (EMNLP 2018 / Microsoft CodeXGLUE)

🛠️ Track 2: Real-World DevOps, Cloud & SysAdmin Benchmark

🦙 Quickstart with Ollama

🛠️ Training Details

Model tree for burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf

Dataset used to train burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf