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

Libraries

How to use google/recurrentgemma-2b-it with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/recurrentgemma-2b-it")
model = AutoModelForCausalLM.from_pretrained("google/recurrentgemma-2b-it")
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 google/recurrentgemma-2b-it with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/google/recurrentgemma-2b-it

SGLang

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

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

ValueError: The device_map provided does not give any device for the following parameters: model.normalizer

by LaferriereJC - opened Apr 11, 2024

Discussion

LaferriereJC

Apr 11, 2024

trying to load in text-generation-webui with latest transformers

ybelkada

Apr 11, 2024

Hi @LaferriereJC
Thanks for the issue! Can you share a reproducible snippet?

LaferriereJC

Apr 11, 2024

•

edited Apr 11, 2024

Installed the latest transformers and attempted to run the boilerplate code on python 3.10.9 on oracle Linux 8.3

I did test w boilerplate code to ensure it wasn't just ooba

aladinggit

Apr 11, 2024

Hi, I encountered the exactly the same problem.

CUDA_VISIBLE_DEVICES=2,3 python try.py ## If I set CUDA_VISIBLE_DEVICES to just one device then it is fine

The code is exactly the snippet provided.
try.py

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/recurrentgemma-2b-it")
model = AutoModelForCausalLM.from_pretrained("google/recurrentgemma-2b-it", device_map="auto")

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))

LaferriereJC

Apr 13, 2024

Successfully installed transformers-4.40.0.dev0

(textgen) [root@pve0 data]# cat recurrentgemma.py
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("/data/text-generation-webui/models/recurrentgemma-2b-it")
model = AutoModelForCausalLM.from_pretrained("/data/text-generation-webui/models/recurrentgemma-2b-it", device_map="auto")

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))

(textgen) [root@pve0 data]# python recurrentgemma.py
Loading checkpoint shards: 100%|███████████████████████████████████████| 2/2 [00:12<00:00, 6.25s/it]
Some weights of RecurrentGemmaForCausalLM were not initialized from the model checkpoint at /data/text-generation-webui/models/recurrentgemma-2b-it and are newly initialized: ['model.normalizer']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Traceback (most recent call last):
File "/data/recurrentgemma.py", line 4, in
model = AutoModelForCausalLM.from_pretrained("/data/text-generation-webui/models/recurrentgemma-2b-it", device_map="auto")
File "/root/miniconda3/envs/textgen/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py", line 563, in from_pretrained
return model_class.from_pretrained(
File "/root/miniconda3/envs/textgen/lib/python3.10/site-packages/transformers/modeling_utils.py", line 3735, in from_pretrained
dispatch_model(model, **device_map_kwargs)
File "/root/miniconda3/envs/textgen/lib/python3.10/site-packages/accelerate/big_modeling.py", line 349, in dispatch_model
check_device_map(model, device_map)
File "/root/miniconda3/envs/textgen/lib/python3.10/site-packages/accelerate/utils/modeling.py", line 1296, in check_device_map
raise ValueError(
ValueError: The device_map provided does not give any device for the following parameters: model.normalizer
(textgen) [root@pve0 data]#

SEONGRYEONG

Apr 17, 2024

I also have the same issue.

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/recurrentgemma-2b-it")
model = AutoModelForCausalLM.from_pretrained("google/recurrentgemma-2b-it", 
                                             device_map="auto",
                                            #  max_memory = {
                                            #      1:"10000MB",
                                            #      2:"10000MB",
                                            #      3:"15000MB"
                                            #  }
                                             )

$nvcc -V

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Mon_Apr__3_17:16:06_PDT_2023
Cuda compilation tools, release 12.1, V12.1.105
Build cuda_12.1.r12.1/compiler.32688072_0

$pip list | grep transformers