Instructions to use NotASI/Gemma-2-9B-Alpaca-QDoRA-step120 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NotASI/Gemma-2-9B-Alpaca-QDoRA-step120 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="NotASI/Gemma-2-9B-Alpaca-QDoRA-step120")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NotASI/Gemma-2-9B-Alpaca-QDoRA-step120")
model = AutoModelForCausalLM.from_pretrained("NotASI/Gemma-2-9B-Alpaca-QDoRA-step120")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NotASI/Gemma-2-9B-Alpaca-QDoRA-step120 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NotASI/Gemma-2-9B-Alpaca-QDoRA-step120"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NotASI/Gemma-2-9B-Alpaca-QDoRA-step120",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NotASI/Gemma-2-9B-Alpaca-QDoRA-step120

SGLang

How to use NotASI/Gemma-2-9B-Alpaca-QDoRA-step120 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 "NotASI/Gemma-2-9B-Alpaca-QDoRA-step120" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NotASI/Gemma-2-9B-Alpaca-QDoRA-step120",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "NotASI/Gemma-2-9B-Alpaca-QDoRA-step120" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NotASI/Gemma-2-9B-Alpaca-QDoRA-step120",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use NotASI/Gemma-2-9B-Alpaca-QDoRA-step120 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 NotASI/Gemma-2-9B-Alpaca-QDoRA-step120 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 NotASI/Gemma-2-9B-Alpaca-QDoRA-step120 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NotASI/Gemma-2-9B-Alpaca-QDoRA-step120 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="NotASI/Gemma-2-9B-Alpaca-QDoRA-step120",
    max_seq_length=2048,
)

Docker Model Runner
How to use NotASI/Gemma-2-9B-Alpaca-QDoRA-step120 with Docker Model Runner:
```
docker model run hf.co/NotASI/Gemma-2-9B-Alpaca-QDoRA-step120
```

Uploaded model

Developed by: NotAiLOL
License: apache-2.0
Finetuned from model : unsloth/gemma-2-9b-bnb-4bit

This gemma2 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Details

This model is fine tuned from unsloth/gemma-2-9b-bnb-4bit on the alpaca-cleaned dataset using the QDoRA method.

This model achieved a loss of 0.817200 on the alpaca-cleaned dataset after step 120.

This model follows the alpaca prompt:

Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}