Local Gemma3 as VSCode Code Generation Extension
To use the #Gemma3:1B model directly in #VSCode as a #codeassistant, you'll need to set up a local inference server or use an API that integrates with VS Code.
Here's a step-by-step guide:
Option 1: Run Gemma Locally & Integrate with VS Code
1. Install Required Dependencies
Ensure you have Python (≥3.9) and `pip` installed. Then, install the necessary packages:
```bash
pip install transformers torch sentencepiece
```
2. Load Gemma 3:1B in a Python Script
Create a Python script (`gemma_inference.py`) to load the model:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "google/gemma-3-1b-it" # or "google/gemma-3-7b-it" if you have more resources
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
def generate_code(prompt):
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_length=200)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Test
print(generate_code("Write a Python function to reverse a string."))
```
3. Set Up a Local API (Optional)
If you want to interact with the model via HTTP (e.g., for VS Code extensions), use `FastAPI`:
```bash
pip install fastapi uvicorn
```
Create `api.py`:
```python
from fastapi import FastAPI
from gemma_inference import generate_code
app = FastAPI()
@app.post("/generate")
async def generate(prompt: str):
return {"response": generate_code(prompt)}
```
Run it:
```bash
uvicorn api:app --reload
```
4. Integrate with VS Code
- Option A: Use the REST Client extension to send requests to `http://localhost:8000/generate`.
- Option B: Create a VS Code extension that calls the API (requires JavaScript/TypeScript knowledge).
---
Option 2: Use an Existing VS Code Extension
If you don’t want to run Gemma locally:
1. Use Google’s #Vertex AI (if Gemma is available there) and call its API.
2. Use Ollama :
```bash
ollama pull gemma3:1b
ollama run gemma3:1b
```
3. Use Continue.dev (VS Code extension that supports local #LLMs).
---
Option 3: Use Hugging Face Inference API (Cloud)
If you prefer not to run the model locally:
1. Get a Hugging Face API key.
2. Use the `huggingface_hub` package:
```python
from huggingface_hub import InferenceClient
client = InferenceClient(token="YOUR_HF_TOKEN")
output = client.text_generation("Write Python code for a linked list.")
print(output)
```
---
Performance Notes
- Gemma 3:1B is small enough to run on a consumer GPU (e.g., RTX 3060+) or CPU but is slow.
- For VS Code autocomplete, consider building a custom extension that queries the model on-the-fly.
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