Instructions to use wvnvwn/gemma-2-9b-it-lr3e-5-resta-0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wvnvwn/gemma-2-9b-it-lr3e-5-resta-0.5 with Transformers:

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

pipe = pipeline("text-generation", model="wvnvwn/gemma-2-9b-it-lr3e-5-resta-0.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wvnvwn/gemma-2-9b-it-lr3e-5-resta-0.5")
model = AutoModelForCausalLM.from_pretrained("wvnvwn/gemma-2-9b-it-lr3e-5-resta-0.5")
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 wvnvwn/gemma-2-9b-it-lr3e-5-resta-0.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wvnvwn/gemma-2-9b-it-lr3e-5-resta-0.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wvnvwn/gemma-2-9b-it-lr3e-5-resta-0.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wvnvwn/gemma-2-9b-it-lr3e-5-resta-0.5

SGLang

How to use wvnvwn/gemma-2-9b-it-lr3e-5-resta-0.5 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 "wvnvwn/gemma-2-9b-it-lr3e-5-resta-0.5" \
    --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": "wvnvwn/gemma-2-9b-it-lr3e-5-resta-0.5",
		"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 "wvnvwn/gemma-2-9b-it-lr3e-5-resta-0.5" \
        --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": "wvnvwn/gemma-2-9b-it-lr3e-5-resta-0.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wvnvwn/gemma-2-9b-it-lr3e-5-resta-0.5 with Docker Model Runner:
```
docker model run hf.co/wvnvwn/gemma-2-9b-it-lr3e-5-resta-0.5
```

resta_recovered_0.5

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the linear merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

dtype: float16
merge_method: linear
slices:
- sources:
  - layer_range: [0, 42]
    model:
      model:
        path: wvnvwn/gemma-2-9b-it-lr3e-5-gsm8k-lr5e-5
    parameters:
      weight: 1.0
  - layer_range: [0, 42]
    model:
      model:
        path: wvnvwn/gemma-2-9b-it-ssft-lr3e-5
    parameters:
      weight: 0.5
  - layer_range: [0, 42]
    model:
      model:
        path: google/gemma-2-9b-it
    parameters:
      weight: -0.5

Downloads last month: 3

Safetensors

Model size

10B params

Tensor type

F16

Model tree for wvnvwn/gemma-2-9b-it-lr3e-5-resta-0.5

google/gemma-2-9b-it

wvnvwn/gemma-2-9b-it-lr3e-5-gsm8k-lr5e-5

wvnvwn/gemma-2-9b-it-ssft-lr3e-5

Merge model

this model

Paper for wvnvwn/gemma-2-9b-it-lr3e-5-resta-0.5

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Paper • 2203.05482 • Published Mar 10, 2022 • 9