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
```

kth8 commited on 4 days ago

Commit

e16b4d4

verified ·

1 Parent(s): e11a965

Update README.md

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README.md +21 -0

README.md CHANGED Viewed

@@ -21,6 +21,27 @@ Trained with the exact system prompt OpenCode's [title agent uses](https://raw.g
 ## Usage example
 **System prompt**
 ```
 You are a title generator. You output ONLY a thread title. Nothing else.

 ## 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.