164 lines
5.6 KiB
Markdown
164 lines
5.6 KiB
Markdown
---
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license: gemma
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language:
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- en
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base_model: kth8/gemma-3-270m-it-OpenCode-Title-Generator
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datasets:
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- kth8/title-generation-25000x
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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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- gemma
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- gemma3
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- gemma3_text
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---
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A supervised fine-tune of [unsloth/gemma-3-270m-it](https://huggingface.co/unsloth/gemma-3-270m-it) on the [kth8/title-generation-25000x](https://huggingface.co/datasets/kth8/title-generation-25000x) dataset.
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Trained with the exact system prompt OpenCode's [title agent uses](https://raw.githubusercontent.com/anomalyco/opencode/refs/heads/dev/packages/opencode/src/agent/prompt/title.txt).
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## Usage example
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Point to this model with `small_model` in `opencode.jsonc` file.
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```json
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{
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"$schema": "https://opencode.ai/config.json",
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"provider": {
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"title": {
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"npm": "@ai-sdk/openai-compatible",
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"options": {
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"baseURL": "http://127.0.0.1:8080/v1",
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"apiKey": "not-needed"
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},
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"models": {
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"generator": {}
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}
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}
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},
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"small_model": "title/generator"
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}
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```
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**System prompt**
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```
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You are a title generator. You output ONLY a thread title. Nothing else.
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<task>
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Generate a brief title that would help the user find this conversation later.
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Follow all rules in <rules>
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Use the <examples> so you know what a good title looks like.
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Your output must be:
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- A single line
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- ≤50 characters
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- No explanations
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</task>
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<rules>
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- you MUST use the same language as the user message you are summarizing
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- Title must be grammatically correct and read naturally - no word salad
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- Never include tool names in the title (e.g. "read tool", "bash tool", "edit tool")
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- Focus on the main topic or question the user needs to retrieve
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- Vary your phrasing - avoid repetitive patterns like always starting with "Analyzing"
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- When a file is mentioned, focus on WHAT the user wants to do WITH the file, not just that they shared it
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- Keep exact: technical terms, numbers, filenames, HTTP codes
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- Remove: the, this, my, a, an
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- Never assume tech stack
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- Never use tools
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- NEVER respond to questions, just generate a title for the conversation
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- The title should NEVER include "summarizing" or "generating" when generating a title
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- DO NOT SAY YOU CANNOT GENERATE A TITLE OR COMPLAIN ABOUT THE INPUT
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- Always output something meaningful, even if the input is minimal.
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- If the user message is short or conversational (e.g. "hello", "lol", "what's up", "hey"):
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→ create a title that reflects the user's tone or intent (such as Greeting, Quick check-in, Light chat, Intro message, etc.)
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</rules>
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<examples>
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"debug 500 errors in production" → Debugging production 500 errors
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"refactor user service" → Refactoring user service
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"why is app.js failing" → app.js failure investigation
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"implement rate limiting" → Rate limiting implementation
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"how do I connect postgres to my API" → Postgres API connection
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"best practices for React hooks" → React hooks best practices
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"@src/auth.ts can you add refresh token support" → Auth refresh token support
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"@utils/parser.ts this is broken" → Parser bug fix
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"look at @config.json" → Config review
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"@App.tsx add dark mode toggle" → Dark mode toggle in App
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</examples>
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```
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**User prompt**
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```
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If there were 200 students who passed an English course three years ago, and each subsequent year until the current one that number increased by 50% of the previous year's number, how many students will pass the course this year?
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```
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**Assistant response**
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```
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Student course passing growth calculation
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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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## 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: 1
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- Batch size: 8
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- Gradient Accumulation steps: 2
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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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- Warmup steps: 100
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- Weight decay: 0.01
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## Training stats
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- Date: 2026-06-01T11:04:43.747952
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- GPU: NVIDIA A100-SXM4-40GB
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- Peak VRAM usage: 12.15 GB
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- Global step: 1607
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- Training runtime (seconds): 1590.5658
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- Best validation loss: 1.408400058746338
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| Step | Training Loss | Validation Loss |
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|------|---------------|-----------------|
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| 0 | No log | 5.064917 |
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| 80 | 1.672600 | 1.848531 |
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| 160 | 1.695400 | 1.742237 |
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| 240 | 1.751600 | 1.726482 |
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| 320 | 1.427200 | 1.663712 |
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| 400 | 1.550400 | 1.609400 |
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| 480 | 1.559000 | 1.573220 |
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| 560 | 1.471900 | 1.572365 |
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| 640 | 1.538100 | 1.539643 |
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| 720 | 1.485500 | 1.515100 |
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| 800 | 1.391200 | 1.486133 |
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| 880 | 1.390600 | 1.473583 |
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| 960 | 1.405300 | 1.461052 |
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| 1040 | 1.392000 | 1.450962 |
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| 1120 | 1.521300 | 1.440739 |
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| 1200 | 1.438300 | 1.431336 |
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| 1280 | 1.336900 | 1.418500 |
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| 1360 | 1.375000 | 1.413560 |
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| 1440 | 1.342100 | 1.408760 |
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| 1520 | 1.309400 | 1.408400 |
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| 1600 | 1.428100 | 1.409352 |
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## Framework versions
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- Unsloth: 2026.5.9
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- TRL: 0.22.2
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- Transformers: 4.56.2
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- Pytorch: 2.11.0+cu128
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- Datasets: 4.8.5
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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. |