Instructions to use kth8/gemma-3-270m-it-Conversation-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kth8/gemma-3-270m-it-Conversation-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="kth8/gemma-3-270m-it-Conversation-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kth8/gemma-3-270m-it-Conversation-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kth8/gemma-3-270m-it-Conversation-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 kth8/gemma-3-270m-it-Conversation-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf kth8/gemma-3-270m-it-Conversation-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kth8/gemma-3-270m-it-Conversation-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf kth8/gemma-3-270m-it-Conversation-GGUF:BF16

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 kth8/gemma-3-270m-it-Conversation-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf kth8/gemma-3-270m-it-Conversation-GGUF:BF16

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 kth8/gemma-3-270m-it-Conversation-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kth8/gemma-3-270m-it-Conversation-GGUF:BF16

Use Docker

docker model run hf.co/kth8/gemma-3-270m-it-Conversation-GGUF:BF16

LM Studio
Jan

vLLM

How to use kth8/gemma-3-270m-it-Conversation-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kth8/gemma-3-270m-it-Conversation-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": "kth8/gemma-3-270m-it-Conversation-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kth8/gemma-3-270m-it-Conversation-GGUF:BF16

SGLang

How to use kth8/gemma-3-270m-it-Conversation-GGUF 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 "kth8/gemma-3-270m-it-Conversation-GGUF" \
    --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": "kth8/gemma-3-270m-it-Conversation-GGUF",
		"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 "kth8/gemma-3-270m-it-Conversation-GGUF" \
        --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": "kth8/gemma-3-270m-it-Conversation-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use kth8/gemma-3-270m-it-Conversation-GGUF with Ollama:
```
ollama run hf.co/kth8/gemma-3-270m-it-Conversation-GGUF:BF16
```
Unsloth Desktop
Docker Model Runner
How to use kth8/gemma-3-270m-it-Conversation-GGUF with Docker Model Runner:
```
docker model run hf.co/kth8/gemma-3-270m-it-Conversation-GGUF:BF16
```

Lemonade

How to use kth8/gemma-3-270m-it-Conversation-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kth8/gemma-3-270m-it-Conversation-GGUF:BF16

Run and chat with the model

lemonade run user.gemma-3-270m-it-Conversation-GGUF-BF16

List all available models

lemonade list

Atomic Chat

A supervised fine-tune of unsloth/gemma-3-270m-it on the kth8/multi-turn-conversation-50000x dataset.

Usage example

System prompt

You are a helpful assistant.

User prompt

Hey there! How's it going?

Assistant response

Hey! I'm doing great, thanks for asking! I'm here and ready to help with whatever you need. What's on your mind today?

Model Details

Base Model: unsloth/gemma-3-270m-it
Parameter Count: 268,098,176
Precision: torch.bfloat16

Training Settings

PEFT

Rank: 32
LoRA alpha: 64
Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Gradient checkpointing: unsloth

SFT

Epoch: 1
Batch size: 8
Gradient Accumulation steps: 2
Learning rate: 0.0002
Optimizer: adamw_torch_fused
Learning rate scheduler: cosine
Warmup steps: 100
Weight decay: 0.01

Training stats

Date: 2026-06-16T15:15:17.789826
GPU: NVIDIA L4
Peak VRAM usage: 16.455 GB
Global step: 3120
Training runtime (seconds): 18261.5241
Best validation loss: 1.666245937347412

Step	Training Loss	Validation Loss
0	No log	2.784440
155	1.882700	1.881819
310	1.805000	1.832387
465	1.803100	1.804098
620	1.781600	1.782886
775	1.785700	1.765646
930	1.776400	1.749293
1085	1.753500	1.736082
1240	1.732600	1.725711
1395	1.703100	1.715472
1550	1.730700	1.705917
1705	1.713500	1.697924
1860	1.725500	1.690107
2015	1.707200	1.684427
2170	1.687700	1.678853
2325	1.675800	1.674952
2480	1.723100	1.671108
2635	1.684300	1.668909
2790	1.692800	1.667304
2945	1.663200	1.666461
3100	1.676500	1.666246

Framework versions

Unsloth: 2026.6.7
TRL: 0.22.2
Transformers: 4.56.2
Pytorch: 2.11.0+cu128
Datasets: 5.0.0
Tokenizers: 0.22.2

License

This model is released under the Gemma license. See the Gemma Terms of Use and Prohibited Use Policy regarding the use of Gemma-generated content.

Downloads last month: 6

GGUF

Model size

0.3B params

Architecture

gemma3

Hardware compatibility

16-bit

Model tree for kth8/gemma-3-270m-it-Conversation-GGUF

Base model

google/gemma-3-270m

Finetuned

google/gemma-3-270m-it

Finetuned

unsloth/gemma-3-270m-it

Finetuned

kth8/gemma-3-270m-it-Conversation

Quantized

(1)

this model

kth8
/

gemma-3-270m-it-Conversation-GGUF

Usage example

Model Details

Training Settings

PEFT

SFT

Training stats

Framework versions

License

Model tree for kth8/gemma-3-270m-it-Conversation-GGUF

Dataset used to train kth8/gemma-3-270m-it-Conversation-GGUF