Instructions to use mujo-labs/sandman-gemma3-1b-multitask-v2-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mujo-labs/sandman-gemma3-1b-multitask-v2-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-3-1b-it-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "mujo-labs/sandman-gemma3-1b-multitask-v2-lora")

Transformers

How to use mujo-labs/sandman-gemma3-1b-multitask-v2-lora with Transformers:

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

pipe = pipeline("text-generation", model="mujo-labs/sandman-gemma3-1b-multitask-v2-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mujo-labs/sandman-gemma3-1b-multitask-v2-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mujo-labs/sandman-gemma3-1b-multitask-v2-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mujo-labs/sandman-gemma3-1b-multitask-v2-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": "mujo-labs/sandman-gemma3-1b-multitask-v2-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mujo-labs/sandman-gemma3-1b-multitask-v2-lora

SGLang

How to use mujo-labs/sandman-gemma3-1b-multitask-v2-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 "mujo-labs/sandman-gemma3-1b-multitask-v2-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": "mujo-labs/sandman-gemma3-1b-multitask-v2-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 "mujo-labs/sandman-gemma3-1b-multitask-v2-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": "mujo-labs/sandman-gemma3-1b-multitask-v2-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use mujo-labs/sandman-gemma3-1b-multitask-v2-lora 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 mujo-labs/sandman-gemma3-1b-multitask-v2-lora 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 mujo-labs/sandman-gemma3-1b-multitask-v2-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mujo-labs/sandman-gemma3-1b-multitask-v2-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="mujo-labs/sandman-gemma3-1b-multitask-v2-lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use mujo-labs/sandman-gemma3-1b-multitask-v2-lora with Docker Model Runner:
```
docker model run hf.co/mujo-labs/sandman-gemma3-1b-multitask-v2-lora
```

sandman-gemma3-1b-multitask-v2-lora / chat_template.jinja

jcblw

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	{{ bos_token }}
	{%- if messages[0]['role'] == 'system' -%}
	{%- if messages[0]['content'] is string -%}
	{%- set first_user_prefix = messages[0]['content'] + '

	' -%}
	{%- else -%}
	{%- set first_user_prefix = messages[0]['content'][0]['text'] + '

	' -%}
	{%- endif -%}
	{%- set loop_messages = messages[1:] -%}
	{%- else -%}
	{%- set first_user_prefix = "" -%}
	{%- set loop_messages = messages -%}
	{%- endif -%}
	{%- for message in loop_messages -%}
	{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
	{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
	{%- endif -%}
	{%- if (message['role'] == 'assistant') -%}
	{%- set role = "model" -%}
	{%- else -%}
	{%- set role = message['role'] -%}
	{%- endif -%}
	{{ '<start_of_turn>' + role + '
	' + (first_user_prefix if loop.first else "") }}
	{%- if message['content'] is string -%}
	{{ message['content'] \| trim }}
	{%- elif message['content'] is iterable -%}
	{%- for item in message['content'] -%}
	{%- if item['type'] == 'image' -%}
	{{ '<start_of_image>' }}
	{%- elif item['type'] == 'text' -%}
	{{ item['text'] \| trim }}
	{%- endif -%}
	{%- endfor -%}
	{%- else -%}
	{{ raise_exception("Invalid content type") }}
	{%- endif -%}
	{{ '<end_of_turn>
	' }}
	{%- endfor -%}
	{%- if add_generation_prompt -%}
	{{'<start_of_turn>model
	'}}
	{%- endif -%}