Instructions to use mjpsm/qwen3-0.6-bash-experiment-model-final-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mjpsm/qwen3-0.6-bash-experiment-model-final-merged with Transformers:

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

pipe = pipeline("text-generation", model="mjpsm/qwen3-0.6-bash-experiment-model-final-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("mjpsm/qwen3-0.6-bash-experiment-model-final-merged")
model = AutoModel.from_pretrained("mjpsm/qwen3-0.6-bash-experiment-model-final-merged", device_map="auto")
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 Settings

vLLM

How to use mjpsm/qwen3-0.6-bash-experiment-model-final-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mjpsm/qwen3-0.6-bash-experiment-model-final-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mjpsm/qwen3-0.6-bash-experiment-model-final-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mjpsm/qwen3-0.6-bash-experiment-model-final-merged

SGLang

How to use mjpsm/qwen3-0.6-bash-experiment-model-final-merged 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 "mjpsm/qwen3-0.6-bash-experiment-model-final-merged" \
    --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": "mjpsm/qwen3-0.6-bash-experiment-model-final-merged",
		"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 "mjpsm/qwen3-0.6-bash-experiment-model-final-merged" \
        --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": "mjpsm/qwen3-0.6-bash-experiment-model-final-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mjpsm/qwen3-0.6-bash-experiment-model-final-merged with Docker Model Runner:
```
docker model run hf.co/mjpsm/qwen3-0.6-bash-experiment-model-final-merged
```

Qwen3 0.6B Bash Experiment Model (Merged)

Overview

This model is a merged fine-tune of Qwen3-0.6B trained to predict bash tool calls from natural language requests.

The objective of this project was to understand how modern AI tool calling systems work under the hood by building a custom dataset, fine-tuning a language model, and deploying it as an API.

This repository contains a standalone merged model and does not require PEFT or LoRA loading at runtime.

Base Model

Qwen/Qwen3-0.6B

Training Method

The model was trained using:

Supervised Fine-Tuning (SFT)
LoRA
PEFT
Transformers
TRL

The adapter was later merged into the base model to create a standalone deployment-ready model.

Dataset

Dataset size:

1,658 training examples

Supported command categories:

Navigation

pwd
ls
ls -la

System Information

whoami
hostname
uname -a

File Creation

mkdir
touch

Training examples consisted of:

Natural Language Request

↓

Tool Call

Example:

User:

what is my system information

Model:

<tool_call>
{"name":"bash","arguments":{"command":"uname -a"}}
</tool_call>

Evaluation

Evaluation Accuracy:

1.0

Example:

Prompt:

what is my systems information

Output:

<tool_call>
{"name":"bash","arguments":{"command":"uname -a"}}
</tool_call>

Intended Use

This model is intended for:

Tool calling experiments
AI agent development
Bash command prediction
Function calling research
Educational projects

Limitations

The model was trained on a small command set and is not intended to be a general purpose bash assistant.

It predicts commands only from the categories present in the training data.

Author

Mazamesso Meba

GitHub: https://github.com/mjpsm05

Hugging Face: https://huggingface.co/mjpsm

Downloads last month: 7

Safetensors

Model size

0.6B params

Tensor type

F16

Model tree for mjpsm/qwen3-0.6-bash-experiment-model-final-merged

Base model

Qwen/Qwen3-0.6B-Base

Finetuned