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

Libraries

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

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

pipe = pipeline("text-generation", model="google/gemma-2b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b")

llama-cpp-python

How to use google/gemma-2b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="google/gemma-2b",
	filename="gemma-2b.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use google/gemma-2b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf google/gemma-2b
# Run inference directly in the terminal:
llama-cli -hf google/gemma-2b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf google/gemma-2b
# Run inference directly in the terminal:
llama-cli -hf google/gemma-2b

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf google/gemma-2b
# Run inference directly in the terminal:
./llama-cli -hf google/gemma-2b

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf google/gemma-2b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf google/gemma-2b

Use Docker

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

LM Studio
Jan

vLLM

How to use google/gemma-2b with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

How to use google/gemma-2b 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 "google/gemma-2b" \
    --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": "google/gemma-2b",
		"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 "google/gemma-2b" \
        --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": "google/gemma-2b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use google/gemma-2b with Ollama:
```
ollama run hf.co/google/gemma-2b
```

Unsloth Studio new

How to use google/gemma-2b 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 google/gemma-2b 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 google/gemma-2b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for google/gemma-2b to start chatting

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

Lemonade

How to use google/gemma-2b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull google/gemma-2b

Run and chat with the model

lemonade run user.gemma-2b-{{QUANT_TAG}}

List all available models

lemonade list

Can't reproduce hellaswag result - getting 42.3% v.s. 71.4 % reported

#67

by robgarct - opened Jun 28, 2024

Discussion

robgarct

Jun 28, 2024

•

edited Jun 28, 2024

Hi! hope all is good.

I'm trying to reproduce the hellaswag result obtained through lm-evaluation-harness. Following the discussion from https://huggingface.co/google/gemma-2b/discussions/18, I:

Pulled lm-evaluation-harness form commit b281b0921b636bc36ad05c0b0b0763bd6dd43463 and set it up in a fresh conda environment
Ran:

$ python main.py --model hf-causal-experimental  --model_args pretrained=google/gemma-2b,dtype=float32  --tasks hellaswag  --device cuda:0  --batch_size 1

Got the following results:

{
  "results": {
    "hellaswag": {
      "acc": 0.34116709818761204,
      "acc_stderr": 0.0047313244091332675,
      "acc_norm": 0.42342162915753834,
      "acc_norm_stderr": 0.004930911515084784
    }
  },
  "versions": {
    "hellaswag": 0
  },
  "config": {
    "model": "hf-causal-experimental",
    "model_args": "pretrained=google/gemma-2b,dtype=float32",
    "num_fewshot": 0,
    "batch_size": "1",
    "batch_sizes": [],
    "device": "cuda:0",
    "no_cache": false,
    "limit": null,
    "bootstrap_iters": 100000,
    "description_dict": {}
  }
}
hf-causal-experimental (pretrained=google/gemma-2b,dtype=float32), limit: None, provide_description: False, num_fewshot: 0, batch_size: 1
|  Task   |Version| Metric |Value |   |Stderr|
|---------|------:|--------|-----:|---|-----:|
|hellaswag|      0|acc     |0.3412|±  |0.0047|
|         |       |acc_norm|0.4234|±  |0.0049|

Am I doing something obviously wrong? As can be seen from the output, I'm getting an accuracy of 42.3%. However, the paper reports an accuracy of 71.4% in hellaswag (similar to the 71.77 listed in the open llm leaderboard: https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard).

Thanks in advance!

robgarct

Jun 28, 2024

•

edited Jun 28, 2024

Taking a deeper look at the results from the open llm leaderboard (https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard, https://huggingface.co/datasets/open-llm-leaderboard-old/details_google__gemma-2b), from those links, if I'm reading this correctly, it seems like the 71.77% accuracy listed in the open llm leaderboard for hellaswag was obtained using 10 few-shot examples per:

...
    "harness|hellaswag|10": {
      "hashes": {
        "hash_examples": "e1768ecb99d7ecf0",
        "hash_full_prompts": "0b4c16983130f84f",
        "hash_input_tokens": "11490eb47260730b",
        "hash_cont_tokens": "6a8516a792e1673e"
      },
      "truncated": 0,
      "non_truncated": 10042,
      "padded": 40055,
      "non_padded": 113,
      "effective_few_shots": 10.0,
      "num_truncated_few_shots": 0
    },
...

Is this read correct?

Also, as noted above, the paper (https://arxiv.org/pdf/2403.08295) and its hugging face page (https://huggingface.co/google/gemma-2b) list a similar accuracy (71.4%) for hellaswag, but note that it was obtained using 0-shot. Is there any way to possibly replicate the 0-shot results listed there through lm-eval-harness or lighteval?

Thanks in advance

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