Instructions to use lemon07r/Gemma-2-Ataraxy-v3i-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lemon07r/Gemma-2-Ataraxy-v3i-9B with Transformers:

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

pipe = pipeline("text-generation", model="lemon07r/Gemma-2-Ataraxy-v3i-9B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lemon07r/Gemma-2-Ataraxy-v3i-9B")
model = AutoModelForCausalLM.from_pretrained("lemon07r/Gemma-2-Ataraxy-v3i-9B", device_map="auto")
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 lemon07r/Gemma-2-Ataraxy-v3i-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lemon07r/Gemma-2-Ataraxy-v3i-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lemon07r/Gemma-2-Ataraxy-v3i-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lemon07r/Gemma-2-Ataraxy-v3i-9B

SGLang

How to use lemon07r/Gemma-2-Ataraxy-v3i-9B 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 "lemon07r/Gemma-2-Ataraxy-v3i-9B" \
    --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": "lemon07r/Gemma-2-Ataraxy-v3i-9B",
		"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 "lemon07r/Gemma-2-Ataraxy-v3i-9B" \
        --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": "lemon07r/Gemma-2-Ataraxy-v3i-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lemon07r/Gemma-2-Ataraxy-v3i-9B with Docker Model Runner:
```
docker model run hf.co/lemon07r/Gemma-2-Ataraxy-v3i-9B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Gemma-2-Ataraxy-v3i-9B

Another experimental model. This one is in the vein of advanced 2.1, but we replace the simpo model used in the original recipe, with a different simpo model, that was more finetuned with writing in mind, ifable. We also use another writing model, which was trained on gutenberg. We use this one at a higher density because SPPO, on paper is the superior training method, to simpo, and quite frankly, ifable is finicky to work with, and can end up being a little too strong.. or heavy in merges. It's a very strong writer but it introduced quite a bit slop in v2.

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

GGUF

https://huggingface.co/lemon07r/Gemma-2-Ataraxy-v3i-9B-Q8_0-GGUF

Merge Details

Merge Method

This model was merged using the della merge method using unsloth/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:

base_model: unsloth/gemma-2-9b-it
dtype: bfloat16
merge_method: della
parameters:
  epsilon: 0.1
  int8_mask: 1.0
  lambda: 1.0
  normalize: 1.0
slices:
- sources:
  - layer_range: [0, 42]
    model: unsloth/gemma-2-9b-it
  - layer_range: [0, 42]
    model: wzhouad/gemma-2-9b-it-WPO-HB
    parameters:
      density: 0.55
      weight: 0.6
  - layer_range: [0, 42]
    model: nbeerbower/Gemma2-Gutenberg-Doppel-9B
    parameters:
      density: 0.35
      weight: 0.6
  - layer_range: [0, 42]
    model: ifable/gemma-2-Ifable-9B
    parameters:
      density: 0.25
      weight: 0.4