Instructions to use CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1 with Transformers:

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

pipe = pipeline("text-generation", model="CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1")
model = AutoModelForMultimodalLM.from_pretrained("CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1")
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 CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1",
	filename="gemma_fintune.gguf",
)

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1
# Run inference directly in the terminal:
llama-cli -hf CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1
# Run inference directly in the terminal:
llama-cli -hf CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1

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 CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1
# Run inference directly in the terminal:
./llama-cli -hf CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1

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 CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1

Use Docker

docker model run hf.co/CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1

LM Studio
Jan

vLLM

How to use CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1

SGLang

How to use CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1 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 "CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1" \
    --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": "CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1",
		"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 "CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1" \
        --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": "CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1 with Ollama:
```
ollama run hf.co/CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1
```

Unsloth Studio

How to use CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1 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 CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1 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 CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1 to start chatting

Atomic Chat new
Docker Model Runner
How to use CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1 with Docker Model Runner:
```
docker model run hf.co/CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1
```

Lemonade

How to use CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1

Run and chat with the model

lemonade run user.gemma_3_1B_reasoning_en_ft_v1-{{QUANT_TAG}}

List all available models

lemonade list

Model Card for CALISTA-INDUSTRY/gemma_3_1B_reasoning_multimodal_en_ft_v2

Model Details

Developed by: Mohammad Yani & Rizky Sulaeman, Politeknik Negeri Indramayu
Model type: Fine-tuned multimodal large language model
Language(s): English
License: Apache 2.0
Finetuned from: gemma-3b

Model Description

gemma_3_1B_reasoning_multimodal_en_ft_v2 is a fine-tuned version of the Gemma3 model, enhanced for multimodal reasoning tasks. It integrates both visual and textual inputs to perform complex reasoning, making it suitable for applications that require understanding and interpreting combined modalities.

Intended Uses & Limitations

Intended Uses

Visual Question Answering (VQA)
Image Captioning
Multimodal Dialogue Systems
Instruction Following with Visual Inputs

Limitations

Performance may degrade on non-English inputs.
May not generalize well to domains significantly different from the training data.
Not suitable for real-time applications without further optimization.

How to Use

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

pipe = pipeline("image-text-to-text", model="CALISTA-INDUSTRY/gemma_3_4B_reasoning_multimodal_en_ft_v2")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

Downloads last month: 16

Safetensors

Model size

1.0B params

Tensor type

BF16

F16

Model tree for CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1

Base model

google/gemma-3-1b-pt

Finetuned

google/gemma-3-1b-it

Quantized

unsloth/gemma-3-1b-it-unsloth-bnb-4bit

Quantized

(37)

this model

CALISTA-INDUSTRY
/

gemma_3_1B_reasoning_en_ft_v1