Instructions to use choprahetarth/gemma-merged-one-layer-only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use choprahetarth/gemma-merged-one-layer-only with Transformers:

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

pipe = pipeline("text-generation", model="choprahetarth/gemma-merged-one-layer-only")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("choprahetarth/gemma-merged-one-layer-only")
model = AutoModelForCausalLM.from_pretrained("choprahetarth/gemma-merged-one-layer-only", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use choprahetarth/gemma-merged-one-layer-only with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/choprahetarth/gemma-merged-one-layer-only

SGLang

How to use choprahetarth/gemma-merged-one-layer-only 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 "choprahetarth/gemma-merged-one-layer-only" \
    --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": "choprahetarth/gemma-merged-one-layer-only",
		"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 "choprahetarth/gemma-merged-one-layer-only" \
        --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": "choprahetarth/gemma-merged-one-layer-only",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use choprahetarth/gemma-merged-one-layer-only with Docker Model Runner:
```
docker model run hf.co/choprahetarth/gemma-merged-one-layer-only
```

Untitled Model (1)

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

Merge Details

Merge Method

This model was merged using the TIES merge method using google/gemma-2-2b as a base.

Models Merged

The following models were included in the merge:

google/gemma-2-2b-it

Configuration

The following YAML configuration was used to produce this model:

base_model: google/gemma-2-2b
dtype: bfloat16
merge_method: ties
parameters:
  int8_mask: 1.0
  normalize: 1.0
slices:
- sources:
  - layer_range: [0, 26]
    model: google/gemma-2-2b
  - layer_range: [0, 26]
    model: google/gemma-2-2b-it
    parameters:
      density:
      - filter: self_attn.o_proj.9
        value: 1.0
      - value: 0.001
      weight:
      - value: 1.0
tokenizer_source: union

Downloads last month: 5

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for choprahetarth/gemma-merged-one-layer-only

google/gemma-2-2b

google/gemma-2-2b-it

Merge model

this model

Paper for choprahetarth/gemma-merged-one-layer-only

Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 19