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

Libraries

How to use kth8/gemma-3-270m-it-Email-Generator with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kth8/gemma-3-270m-it-Email-Generator")
model = AutoModelForCausalLM.from_pretrained("kth8/gemma-3-270m-it-Email-Generator")
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

vLLM

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

Use Docker

docker model run hf.co/kth8/gemma-3-270m-it-Email-Generator

SGLang

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

Unsloth Studio new

How to use kth8/gemma-3-270m-it-Email-Generator 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 kth8/gemma-3-270m-it-Email-Generator 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 kth8/gemma-3-270m-it-Email-Generator to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kth8/gemma-3-270m-it-Email-Generator to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="kth8/gemma-3-270m-it-Email-Generator",
    max_seq_length=2048,
)

Docker Model Runner
How to use kth8/gemma-3-270m-it-Email-Generator with Docker Model Runner:
```
docker model run hf.co/kth8/gemma-3-270m-it-Email-Generator
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

A fine-tune of unsloth/gemma-3-270m-it on the Kamisori-daijin/email-datasets-20k dataset.

Usage example

System prompt

You are a helpful assistant.

User prompt

Write a direct and concise business email(under 200 words) from a Founder to a Angry Shareholder regarding notifying about a price change, specifically after winning a major award.

Assistant response

Subject: Regarding Pricing Update - [Company Name]

Dear [Shareholder Name],

I'm writing to address your concerns regarding our recent pricing update. You’re right to raise questions, and I want to be transparent about the reasoning behind it.

We’re incredibly proud to announce that [Company Name] recently won the [Award Name] award – a significant validation of our innovative product and market position. This achievement, coupled with increased customer demand, has necessitated a modest price adjustment to ensure we can continue investing in product development and deliver the exceptional service our customers expect.

We remain confident in the value we provide and believe this change will ultimately benefit all our stakeholders. We welcome the opportunity to discuss this further with you.

Sincerely,
[Name]
Founder, [Company Name]

Model Details

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

Hardware

GPU: NVIDIA RTX PRO 6000 Blackwell Server Edition
Announced: Mar 17th, 2025
Release Date: Mar 18th, 2025
Memory Type: GDDR7
Bandwidth: 1.79 TB/s
Memory Size: 96 GB
Memory Bus: 512 bit
Shading Units: 24064
TDP: 600W

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: 4
Batch size: 32
Gradient Accumulation steps: 1
Warmup ratio: 0.05
Learning rate: 0.0002
Optimizer: adamw_torch_fused
Learning rate scheduler: cosine

Training stats

Date: 2026-03-25T12:51:43.831886
Peak VRAM usage: 16.834 GB
Global step: 2360
Training runtime (seconds): 470.094
Average training loss: 1.2040837437419567
Final validation loss: 1.2054944038391113

Framework versions

Unsloth: 2026.3.11
TRL: 0.22.2
Transformers: 4.56.2
Pytorch: 2.10.0+cu128
Datasets: 4.8.4
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: 4

Safetensors

Model size

0.3B params

Tensor type

BF16

Model tree for kth8/gemma-3-270m-it-Email-Generator

Base model

google/gemma-3-270m

Finetuned

google/gemma-3-270m-it

Finetuned

unsloth/gemma-3-270m-it

Finetuned

(395)

this model

Quantizations

1 model

kth8
/

gemma-3-270m-it-Email-Generator

Usage example

Model Details

Hardware

Training Settings

PEFT

SFT

Training stats

Framework versions

License

Model tree for kth8/gemma-3-270m-it-Email-Generator

Dataset used to train kth8/gemma-3-270m-it-Email-Generator