Instructions to use gnumanth/gemma-unsloth-alpaca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gnumanth/gemma-unsloth-alpaca with Transformers:

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

pipe = pipeline("text-generation", model="gnumanth/gemma-unsloth-alpaca")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gnumanth/gemma-unsloth-alpaca")
model = AutoModelForCausalLM.from_pretrained("gnumanth/gemma-unsloth-alpaca", device_map="auto")
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 Settings

vLLM

How to use gnumanth/gemma-unsloth-alpaca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gnumanth/gemma-unsloth-alpaca"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gnumanth/gemma-unsloth-alpaca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gnumanth/gemma-unsloth-alpaca

SGLang

How to use gnumanth/gemma-unsloth-alpaca 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 "gnumanth/gemma-unsloth-alpaca" \
    --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": "gnumanth/gemma-unsloth-alpaca",
		"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 "gnumanth/gemma-unsloth-alpaca" \
        --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": "gnumanth/gemma-unsloth-alpaca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use gnumanth/gemma-unsloth-alpaca 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 gnumanth/gemma-unsloth-alpaca 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 gnumanth/gemma-unsloth-alpaca to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for gnumanth/gemma-unsloth-alpaca to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="gnumanth/gemma-unsloth-alpaca",
    max_seq_length=2048,
)

Docker Model Runner
How to use gnumanth/gemma-unsloth-alpaca with Docker Model Runner:
```
docker model run hf.co/gnumanth/gemma-unsloth-alpaca
```

gemma-alpacha

yahma/alpaca-cleaned finetuned with gemma-7b-bnb-4bit

Usage

pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"

from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained("gnumanth/gemma-unsloth-alpaca")

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:
{}"""

FastLanguageModel.for_inference(model) # Enable native 2x faster inference
inputs = tokenizer(
[
    alpaca_prompt.format(
        "Give me a python code for quicksort", # instruction
        "1,-1,0,8,9,-2,2", # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)

<bos>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:
Give me a python code for quicksort

### Input:
1,-1,0,8,9,-2,2

### Response:
def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[0]
    left = [i for i in arr[1:] if i < pivot]
    right = [i for i in arr[1:] if i >= pivot]
    return quicksort(left) + [pivot] + quicksort(right)<eos>

Hemanth HMM | (Built with unsloth)

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Safetensors

Model size

9B params

Tensor type

BF16

Model tree for gnumanth/gemma-unsloth-alpaca

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unsloth/gemma-7b-bnb-4bit

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