Instructions to use NeoChen1024/gemma-2-9B-it-advanced-v2.1-exl2-6.0bpw-h8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NeoChen1024/gemma-2-9B-it-advanced-v2.1-exl2-6.0bpw-h8 with Transformers:

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

pipe = pipeline("text-generation", model="NeoChen1024/gemma-2-9B-it-advanced-v2.1-exl2-6.0bpw-h8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NeoChen1024/gemma-2-9B-it-advanced-v2.1-exl2-6.0bpw-h8")
model = AutoModelForCausalLM.from_pretrained("NeoChen1024/gemma-2-9B-it-advanced-v2.1-exl2-6.0bpw-h8")
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 NeoChen1024/gemma-2-9B-it-advanced-v2.1-exl2-6.0bpw-h8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NeoChen1024/gemma-2-9B-it-advanced-v2.1-exl2-6.0bpw-h8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NeoChen1024/gemma-2-9B-it-advanced-v2.1-exl2-6.0bpw-h8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NeoChen1024/gemma-2-9B-it-advanced-v2.1-exl2-6.0bpw-h8

SGLang

How to use NeoChen1024/gemma-2-9B-it-advanced-v2.1-exl2-6.0bpw-h8 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 "NeoChen1024/gemma-2-9B-it-advanced-v2.1-exl2-6.0bpw-h8" \
    --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": "NeoChen1024/gemma-2-9B-it-advanced-v2.1-exl2-6.0bpw-h8",
		"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 "NeoChen1024/gemma-2-9B-it-advanced-v2.1-exl2-6.0bpw-h8" \
        --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": "NeoChen1024/gemma-2-9B-it-advanced-v2.1-exl2-6.0bpw-h8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NeoChen1024/gemma-2-9B-it-advanced-v2.1-exl2-6.0bpw-h8 with Docker Model Runner:
```
docker model run hf.co/NeoChen1024/gemma-2-9B-it-advanced-v2.1-exl2-6.0bpw-h8
```

Exllamav2 Quant of Gemma Advanced V2.1 (6.0bpw, 8bit head)

This is a merge of the 'smartest' advanced fine-tunes available for Gemma-2-9b-it. It includes WPO, SimPO, and SPPO. The merge was performed via the SOTA 'della' merge method. Merge parameters have been hand-tuned for best results. The Q8_0 quant is highly recommended until better quants come along.

Notes and observations:

The extreme temperature sensitivity from V1 has been fixed, no longer needs to be run at lower temperatures
Has a somewhat different writing style than any of the parent models
Great instruction following
Tracks plot details well and has good situational understanding
Seems to have a good understanding of psychology, emotions and creative writing
More 'sane' than base gemma-it, SPPO, or SimPO - not as prone to 'Cruella De Vil' or 'Evil Sorceress' like SPPO or SimPO, when portraying characters
Would likely serve as a good base for further merges
I'm looking for a job, if you're hiring. I'm a skilled Python developer who brings strong devops skills along with an ever-growing knowledge of machine learning pipelines and models. Message me if you want to talk about what I can bring to your team.
Overall, this feels like a very useful and successful merge.

Quantized GGUFs can be found here:

Thanks to everyone who was kind enough to provide quants!

I'll link to other quants as they appear.

sample ollama Modelfile

FROM /path/to/file/gemma-2-9B-it-advanced-v2.1-Q8_0.gguf
PARAMETER stop "<start_of_turn>"
PARAMETER stop "<end_of_turn>"
PARAMETER num_ctx 8192
TEMPLATE """<start_of_turn>user
{{ if .System }}{{ .System }} {{ end }}{{ .Prompt }}<end_of_turn>
<start_of_turn>model
{{ .Response }}<end_of_turn>"""

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

Merge Details

Merge Method

This model was merged using the della merge method using google/gemma-2-9b-it 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: google/gemma-2-9b-it 
  - model: wzhouad/gemma-2-9b-it-WPO-HB
    parameters:
      density: 0.55
      weight: 0.6
  - model: princeton-nlp/gemma-2-9b-it-SimPO 
    parameters:
      density: 0.35
      weight: 0.6
  - model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
    parameters:
      density: 0.25
      weight: 0.4
merge_method: della
base_model: google/gemma-2-9b-it
parameters:
  normalize: true
  int8_mask: true
  lambda: 1.0
  epsilon: 0.1
dtype: float16

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Model tree for NeoChen1024/gemma-2-9B-it-advanced-v2.1-exl2-6.0bpw-h8

Base model

jsgreenawalt/gemma-2-9B-it-advanced-v2.1

Quantized

(3)

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