Instructions to use espressor/google.gemma-2b-it_W4A16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use espressor/google.gemma-2b-it_W4A16 with Transformers:

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

pipe = pipeline("text-generation", model="espressor/google.gemma-2b-it_W4A16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("espressor/google.gemma-2b-it_W4A16")
model = AutoModelForMultimodalLM.from_pretrained("espressor/google.gemma-2b-it_W4A16")
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 espressor/google.gemma-2b-it_W4A16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "espressor/google.gemma-2b-it_W4A16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "espressor/google.gemma-2b-it_W4A16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/espressor/google.gemma-2b-it_W4A16

SGLang

How to use espressor/google.gemma-2b-it_W4A16 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 "espressor/google.gemma-2b-it_W4A16" \
    --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": "espressor/google.gemma-2b-it_W4A16",
		"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 "espressor/google.gemma-2b-it_W4A16" \
        --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": "espressor/google.gemma-2b-it_W4A16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use espressor/google.gemma-2b-it_W4A16 with Docker Model Runner:
```
docker model run hf.co/espressor/google.gemma-2b-it_W4A16
```

google/gemma-2b-it - W4A16 Compression

This is a compressed model using llmcompressor.

Compression Configuration

Base Model: google/gemma-2b-it
Compression Scheme: W4A16
Dataset: HuggingFaceH4/ultrachat_200k
Dataset Split: train_sft
Number of Samples: 512
Preprocessor: chat
Maximum Sequence Length: 8192

Sample Output

Prompt:

<bos><start_of_turn>user
Who is Alan Turing?<end_of_turn>

Output:

<bos><bos><start_of_turn>user
Who is Alan Turing?<end_of_turn>
Alan Turing was a British mathematician and computer scientist who made significant contributions to the fields of mathematics, computer science, and physics. He is considered one of the pioneers of computer science and a major figure in the history of artificial intelligence.

**Key Contributions:**

* **Coined the term "computer science"**: Turing was one of the first to use the term in a modern sense to refer to the study of the theoretical and practical principles of computation.
* **Developed the Turing machine**: This is a theoretical model of computation that is considered to be the most powerful known model of computation.
* **Pioneered research in artificial

Evaluation

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Safetensors

Model size

3B params

Tensor type

I64

I32

BF16

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