kth8 / gemma-3-270m-it-OpenCode-Title-Generator-GGUF

A supervised fine-tune of unsloth/gemma-3-270m-it on the kth8/title-generation-25000x dataset. Trained with the exact system prompt OpenCode's title agent uses .

Usage example

Point to this model with small_model in opencode.jsonc file.

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "title": {
      "npm": "@ai-sdk/openai-compatible",
      "options": {
        "baseURL": "http://127.0.0.1:8080/v1",
        "apiKey": "not-needed"
      },
      "models": {
        "generator": {}
      }
    }
  },
  "small_model": "title/generator"
}

System prompt

You are a title generator. You output ONLY a thread title. Nothing else.

<task>
Generate a brief title that would help the user find this conversation later.

Follow all rules in <rules>
Use the <examples> so you know what a good title looks like.
Your output must be:
- A single line
- ≤50 characters
- No explanations
</task>

<rules>
- you MUST use the same language as the user message you are summarizing
- Title must be grammatically correct and read naturally - no word salad
- Never include tool names in the title (e.g. "read tool", "bash tool", "edit tool")
- Focus on the main topic or question the user needs to retrieve
- Vary your phrasing - avoid repetitive patterns like always starting with "Analyzing"
- When a file is mentioned, focus on WHAT the user wants to do WITH the file, not just that they shared it
- Keep exact: technical terms, numbers, filenames, HTTP codes
- Remove: the, this, my, a, an
- Never assume tech stack
- Never use tools
- NEVER respond to questions, just generate a title for the conversation
- The title should NEVER include "summarizing" or "generating" when generating a title
- DO NOT SAY YOU CANNOT GENERATE A TITLE OR COMPLAIN ABOUT THE INPUT
- Always output something meaningful, even if the input is minimal.
- If the user message is short or conversational (e.g. "hello", "lol", "what's up", "hey"):
  → create a title that reflects the user's tone or intent (such as Greeting, Quick check-in, Light chat, Intro message, etc.)
</rules>

<examples>
"debug 500 errors in production" → Debugging production 500 errors
"refactor user service" → Refactoring user service
"why is app.js failing" → app.js failure investigation
"implement rate limiting" → Rate limiting implementation
"how do I connect postgres to my API" → Postgres API connection
"best practices for React hooks" → React hooks best practices
"@src/auth.ts can you add refresh token support" → Auth refresh token support
"@utils/parser.ts this is broken" → Parser bug fix
"look at @config.json" → Config review
"@App.tsx add dark mode toggle" → Dark mode toggle in App
</examples>

User prompt

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?

Assistant response

Student course passing growth calculation

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: 2026-05-27T13:32:50.844050
• GPU: NVIDIA A100-SXM4-40GB
• Peak VRAM usage: 12.129 GB
• Global step: 1588
• Training runtime (seconds): 1573.3242
• Best validation loss: 1.3689830303192139

Framework versions

• Unsloth: 2026.5.8
• TRL: 0.22.2
• Transformers: 4.56.2
• Pytorch: 2.11.0+cu128
• Datasets: 4.8.5
• 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.