96 lines
3.0 KiB
Markdown
96 lines
3.0 KiB
Markdown
---
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license: gemma
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language:
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- en
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base_model: unsloth/gemma-3-270m-it
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datasets:
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- Kamisori-daijin/email-datasets-20k
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- sft
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- trl
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- unsloth
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- google
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- gemma
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- gemma3
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- gemma3_text
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---
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A fine-tune of [unsloth/gemma-3-270m-it](https://huggingface.co/unsloth/gemma-3-270m-it) on the [Kamisori-daijin/email-datasets-20k](https://huggingface.co/datasets/Kamisori-daijin/email-datasets-20k) dataset.
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## Usage example
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**System prompt**
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```
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You are a helpful assistant.
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```
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**User prompt**
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```
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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.
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```
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**Assistant response**
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```
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Subject: Regarding Pricing Update - [Company Name]
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Dear [Shareholder Name],
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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.
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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.
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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.
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Sincerely,
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[Name]
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Founder, [Company Name]
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```
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## Model Details
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- Base Model: `unsloth/gemma-3-270m-it`
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- Parameter Count: 268,098,176
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- Precision: torch.bfloat16
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## Hardware
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- GPU: NVIDIA RTX PRO 6000 Blackwell Server Edition
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- Announced: Mar 17th, 2025
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- Release Date: Mar 18th, 2025
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- Memory Type: GDDR7
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- Bandwidth: 1.79 TB/s
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- Memory Size: 96 GB
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- Memory Bus: 512 bit
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- Shading Units: 24064
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- TDP: 600W
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## Training Settings
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### PEFT
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- Rank: 32
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- LoRA alpha: 64
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- Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
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- Gradient checkpointing: unsloth
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### SFT
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- Epoch: 4
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- Batch size: 32
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- Gradient Accumulation steps: 1
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- Warmup ratio: 0.05
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- Learning rate: 0.0002
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- Optimizer: adamw_torch_fused
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- Learning rate scheduler: cosine
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## Training stats
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- Date: 2026-03-25T12:51:43.831886
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- Peak VRAM usage: 16.834 GB
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- Global step: 2360
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- Training runtime (seconds): 470.094
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- Average training loss: 1.2040837437419567
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- Final validation loss: 1.2054944038391113
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## Framework versions
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- Unsloth: 2026.3.11
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- TRL: 0.22.2
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- Transformers: 4.56.2
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- Pytorch: 2.10.0+cu128
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- Datasets: 4.8.4
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- Tokenizers: 0.22.2
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## License
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This model is released under the Gemma license. See the [Gemma Terms of Use](https://ai.google.dev/gemma/terms) and [Prohibited Use Policy](https://policies.google.com/terms/generative-ai/use-policy) regarding the use of Gemma-generated content. |