Instructions to use Natch69/GemOmniscien-ties with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Natch69/GemOmniscien-ties with Transformers:

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

pipe = pipeline("text-generation", model="Natch69/GemOmniscien-ties")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Natch69/GemOmniscien-ties")
model = AutoModelForCausalLM.from_pretrained("Natch69/GemOmniscien-ties")
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 Natch69/GemOmniscien-ties with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Natch69/GemOmniscien-ties"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Natch69/GemOmniscien-ties",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Natch69/GemOmniscien-ties

SGLang

How to use Natch69/GemOmniscien-ties 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 "Natch69/GemOmniscien-ties" \
    --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": "Natch69/GemOmniscien-ties",
		"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 "Natch69/GemOmniscien-ties" \
        --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": "Natch69/GemOmniscien-ties",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Natch69/GemOmniscien-ties with Docker Model Runner:
```
docker model run hf.co/Natch69/GemOmniscien-ties
```

GemOmniscien-ties

GemOmniscien-ties is a merge of the following models using mergekit:

🧩 Configuration

```yaml models:

model: Warit2/GemOmniscien parameters: density: 0.5 weight: 0.5
model: google/gemma-2b-it parameters: density: 0.5 weight: 0.5 # weight gradient merge_method: ties base_model: Warit2/GemOmniscien parameters: normalize: true int8_mask: true dtype: bfloat16

models:

- model: unsloth/gemma-7b-bnb-4bit

layer_range: [0, 32]

# no parameters necessary for base model

- model: mistralai/Mistral-7B-v0.1

layer_range: [24, 32]

merge_method: passthrough

# base_model: unsloth/gemma-7b-bnb-4bit

parameters:

normalize: true

int8_mask: true

dtype: float16

slices:

- sources:

- model: unsloth/gemma-2b-bnb-4bit

layer_range: [0, 16]

- sources:

- model: NousResearch/Nous-Hermes-llama-2-7b

layer_range: [0, 22]

merge_method: passthrough

dtype: bfloat16

models:

- model: unsloth/gemma-2b-bnb-4bit

parameters:

density: 0.53

weight: 0.45

- model: TinyLlama/TinyLlama-1.1B-Chat-v1.0

parameters:

weight: 0.5

merge_method: ties

base_model: unsloth/gemma-2b-bnb-4bit

parameters:

int8_mask: true

dtype: bfloat16

```

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Safetensors

Model size

3B params

Tensor type

BF16