gemma-3-270m-it-Email-Generator/README.md

license, language, base_model, datasets, pipeline_tag, library_name, tags

license	language	base_model	datasets	pipeline_tag	library_name	tags
gemma	en	unsloth/gemma-3-270m-it	Kamisori-daijin/email-datasets-20k	text-generation	transformers	sft trl unsloth google gemma gemma3 gemma3_text

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.