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

Libraries

How to use kth8/gemma-3-270m-it-OpenCode-Title-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-OpenCode-Title-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-OpenCode-Title-Generator")
model = AutoModelForCausalLM.from_pretrained("kth8/gemma-3-270m-it-OpenCode-Title-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-OpenCode-Title-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-OpenCode-Title-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-OpenCode-Title-Generator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Unsloth Studio new

How to use kth8/gemma-3-270m-it-OpenCode-Title-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-OpenCode-Title-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-OpenCode-Title-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-OpenCode-Title-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-OpenCode-Title-Generator",
    max_seq_length=2048,
)

Docker Model Runner
How to use kth8/gemma-3-270m-it-OpenCode-Title-Generator with Docker Model Runner:
```
docker model run hf.co/kth8/gemma-3-270m-it-OpenCode-Title-Generator
```

gemma-3-270m-it-OpenCode-Title-Generator / README.md

kth8

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	---
	license: gemma
	language:
	- en
	base_model: unsloth/gemma-3-270m-it
	datasets:
	- kth8/title-generation-25000x
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- sft
	- trl
	- unsloth
	- gemma
	- gemma3
	- gemma3_text
	---
	![logo](https://storage.googleapis.com/gweb-developer-goog-blog-assets/images/gemma-3_2.original.png)
	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.
	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).

	## Usage example

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

	```json
	{
	"$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

	\| Step \| Training Loss \| Validation Loss \|
	\|------\|--------------\|----------------\|
	\| 79 \| 1.916200 \| 1.783801 \|
	\| 158 \| 1.725300 \| 1.744159 \|
	\| 237 \| 1.693900 \| 1.640494 \|
	\| 316 \| 1.628300 \| 1.608212 \|
	\| 395 \| 1.535700 \| 1.557622 \|
	\| 474 \| 1.525200 \| 1.579373 \|
	\| 553 \| 1.465500 \| 1.528539 \|
	\| 632 \| 1.447900 \| 1.489644 \|
	\| 711 \| 1.572100 \| 1.488969 \|
	\| 790 \| 1.528400 \| 1.472376 \|
	\| 869 \| 1.497800 \| 1.438234 \|
	\| 948 \| 1.476900 \| 1.431505 \|
	\| 1027 \| 1.387800 \| 1.412816 \|
	\| 1106 \| 1.369100 \| 1.401051 \|
	\| 1185 \| 1.286400 \| 1.391667 \|
	\| 1264 \| 1.406300 \| 1.379098 \|
	\| 1343 \| 1.412500 \| 1.374640 \|
	\| 1422 \| 1.321700 \| 1.371226 \|
	\| 1501 \| 1.383900 \| 1.368983 \|
	\| 1580 \| 1.337300 \| 1.369141 \|

	## 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](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.