Instructions to use micrictor/gemma-3-270m-it-ft-bash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use micrictor/gemma-3-270m-it-ft-bash with Transformers:

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

pipe = pipeline("text-generation", model="micrictor/gemma-3-270m-it-ft-bash")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("micrictor/gemma-3-270m-it-ft-bash")
model = AutoModelForCausalLM.from_pretrained("micrictor/gemma-3-270m-it-ft-bash", 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 micrictor/gemma-3-270m-it-ft-bash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "micrictor/gemma-3-270m-it-ft-bash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "micrictor/gemma-3-270m-it-ft-bash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/micrictor/gemma-3-270m-it-ft-bash

SGLang

How to use micrictor/gemma-3-270m-it-ft-bash 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 "micrictor/gemma-3-270m-it-ft-bash" \
    --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": "micrictor/gemma-3-270m-it-ft-bash",
		"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 "micrictor/gemma-3-270m-it-ft-bash" \
        --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": "micrictor/gemma-3-270m-it-ft-bash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use micrictor/gemma-3-270m-it-ft-bash with Docker Model Runner:
```
docker model run hf.co/micrictor/gemma-3-270m-it-ft-bash
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Model Card for checkpoints

This model is a fine-tuned version of google/gemma-3-270m-it. It has been trained using TRL.

Quick start

from transformers import pipeline


prompt = "copy all .jpg files to /mnt/backup"
model_input = {
    "messages": [
          {"role": "system", "content": "You are a helpful assistant that translates natural language to bash commands."},
          {"role": "user", "content": f"Generate single Bash command: {prompt}"},
      ]
}

output = pipe(model_input["messages"], max_new_tokens=256, disable_compile=True, clean_up_tokenization_spaces=False)[0]

generator = pipeline("text-generation", model="micrictor/checkpoints", device="cuda")
output = generator(model_input["messages"],
    max_new_tokens=256,
    disable_compile=True,
    clean_up_tokenization_spaces=False,
    return_full_text=False,
)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

TRL: 0.23.0
Transformers: 4.56.2
Pytorch: 2.8.0+cu129
Datasets: 4.1.1
Tokenizers: 0.22.1

Citations

Cite TRL as:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}

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Safetensors

Model size

0.3B params

Tensor type

BF16

Model tree for micrictor/gemma-3-270m-it-ft-bash

Base model

google/gemma-3-270m

Finetuned

google/gemma-3-270m-it

Finetuned

(1127)

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