Instructions to use espressor/google.gemma-2b-it_W8A8_int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use espressor/google.gemma-2b-it_W8A8_int8 with Transformers:

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

pipe = pipeline("text-generation", model="espressor/google.gemma-2b-it_W8A8_int8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("espressor/google.gemma-2b-it_W8A8_int8")
model = AutoModelForCausalLM.from_pretrained("espressor/google.gemma-2b-it_W8A8_int8")
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 espressor/google.gemma-2b-it_W8A8_int8 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_W8A8_int8"
# 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_W8A8_int8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/espressor/google.gemma-2b-it_W8A8_int8

SGLang

How to use espressor/google.gemma-2b-it_W8A8_int8 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_W8A8_int8" \
    --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_W8A8_int8",
		"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_W8A8_int8" \
        --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_W8A8_int8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use espressor/google.gemma-2b-it_W8A8_int8 with Docker Model Runner:
```
docker model run hf.co/espressor/google.gemma-2b-it_W8A8_int8
```

google/gemma-2b-it - W8A8_int8 Compression

This is a compressed model using llmcompressor.

Compression Configuration

Base Model: google/gemma-2b-it
Compression Scheme: W8A8_int8
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>
 Walkover outlander outlander outlander outlander outlander jetta Walkover deleter hefydVíce Walkover deleterrrrrrr Walkover deletertextThemeNgoài, Walkover affor noten equalled Walkover affor noten equalled Walkover affor solidarNgoài, Walkover affor deleterCodedInputStream into Walkover bettmulticolumnNgoài, Walkover affor Walkover bett snoopy Walkover deleter Walkover deleter Walkover deleter Walkover deleter Walkover deleter Walkover deleter Walkover deleter Walkover deleter Walkover bettEUROPA have Walkover apper Walkover igniNgoài, Walkover affor Walkover deleter Walkover apper Walkover deleter Walkover orlando Walkover equalled Walkover deleter Walkover bett Walkover deleter Walkover bett Walkover deleter Walkover orlando Walkover equalled Walkover deleter Walkover deleter Walkover deleter Walkover deleter Walkover deleter Walkover deleter Walkover bett Walkover orlando Walkover be Walkover deleter Walkover bett Walkover orlando Walkover be Walkover deleter Walkover

Evaluation

Downloads last month: 2

Safetensors

Model size

3B params

Tensor type

BF16

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