Instructions to use vaiv/GeM2-Llamion-14B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vaiv/GeM2-Llamion-14B-Base with Transformers:

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

pipe = pipeline("text-generation", model="vaiv/GeM2-Llamion-14B-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vaiv/GeM2-Llamion-14B-Base")
model = AutoModelForCausalLM.from_pretrained("vaiv/GeM2-Llamion-14B-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 vaiv/GeM2-Llamion-14B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vaiv/GeM2-Llamion-14B-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": "vaiv/GeM2-Llamion-14B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vaiv/GeM2-Llamion-14B-Base

SGLang

How to use vaiv/GeM2-Llamion-14B-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 "vaiv/GeM2-Llamion-14B-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": "vaiv/GeM2-Llamion-14B-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 "vaiv/GeM2-Llamion-14B-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": "vaiv/GeM2-Llamion-14B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use vaiv/GeM2-Llamion-14B-Base with Docker Model Runner:
```
docker model run hf.co/vaiv/GeM2-Llamion-14B-Base
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GeM2-Llamion-14B

We have released Llamion as GeM 2.0, the second series of generative models developed by VAIV Company to address the our principal business needs.

Llamion (Llamafied Orion) is derived from transforming the Orion model into the standard LLaMA architecture through parameter mapping and offline knowledge transfer. Further technical specifications and study results will be detailed in our upcoming paper, available on this page.

Contributors

VAIV Company AI Lab (vaiv.kr)

Downloads last month: 8,302

Safetensors

Model size

14B params

Tensor type

BF16

Model tree for vaiv/GeM2-Llamion-14B-Base

Adapters

15 models

Quantizations

4 models

vaiv
/

GeM2-Llamion-14B-Base

GeM2-Llamion-14B

Contributors

Model tree for vaiv/GeM2-Llamion-14B-Base

Space using vaiv/GeM2-Llamion-14B-Base 1