Instructions to use Sorawiz/Gemma-9B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sorawiz/Gemma-9B-Base with Transformers:

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

pipe = pipeline("text-generation", model="Sorawiz/Gemma-9B-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sorawiz/Gemma-9B-Base")
model = AutoModelForCausalLM.from_pretrained("Sorawiz/Gemma-9B-Base")
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 Sorawiz/Gemma-9B-Base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Sorawiz/Gemma-9B-Base

SGLang

How to use Sorawiz/Gemma-9B-Base 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 "Sorawiz/Gemma-9B-Base" \
    --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": "Sorawiz/Gemma-9B-Base",
		"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 "Sorawiz/Gemma-9B-Base" \
        --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": "Sorawiz/Gemma-9B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Sorawiz/Gemma-9B-Base with Docker Model Runner:
```
docker model run hf.co/Sorawiz/Gemma-9B-Base
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Chat Template

Gemma Instruct

<start_of_turn>user
{prompt}<end_of_turn>
<start_of_turn>model
<end_of_turn>
<start_of_turn>model

{{- $system := "" }}
{{- range .Messages }}
{{- if eq .Role "system" }}
{{- if not $system }}{{ $system = .Content }}
{{- else }}{{ $system = printf "%s\n\n%s" $system .Content }}
{{- end }}
{{- continue }}
{{- else if eq .Role "user" }}<start_of_turn>user
{{- if $system }}
{{ $system }}
{{- $system = "" }}
{{- end }}
{{- else if eq .Role "assistant" }}<start_of_turn>model
{{- end }}
{{ .Content }}<end_of_turn>
{{ end }}<start_of_turn>model

GGUF

Thank you mradermacher for creating the GGUF versions of this model.

Static quants - mradermacher/Gemma-9B-Base-GGUF

Merge

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 zelk12/MT2-Gen6-gemma-2-9B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: prithivMLmods/GWQ-9B-Preview2
    parameters:
      density: 1.00
      weight: 1.00
  - model: OpenMeditron/Meditron3-Gemma2-9B
    parameters:
      density: 1.00
      weight: 1.00
  - model: WiroAI/WiroAI-Finance-Gemma-9B
    parameters:
      density: 1.00
      weight: 1.00
  - model: AXCXEPT/EZO-Humanities-9B-gemma-2-it
    parameters:
      density: 1.00
      weight: 1.00
  - model: FuseAI/FuseChat-Gemma-2-9B-Instruct
    parameters:
      density: 1.00
      weight: 1.00
  - model: Rombo-Org/Rombo-LLM-V2.7-gemma-2-9b
    parameters:
      density: 1.00
      weight: 1.00
merge_method: ties
base_model: zelk12/MT2-Gen6-gemma-2-9B
parameters:
  density: 1
  normalize: true
dtype: bfloat16