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

Libraries

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

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

pipe = pipeline("text-generation", model="vaiv/GeM2-Llamion-14B-LongChat")
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-LongChat")
model = AutoModelForCausalLM.from_pretrained("vaiv/GeM2-Llamion-14B-LongChat")
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 vaiv/GeM2-Llamion-14B-LongChat 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-LongChat"
# 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-LongChat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Docker Model Runner
How to use vaiv/GeM2-Llamion-14B-LongChat with Docker Model Runner:
```
docker model run hf.co/vaiv/GeM2-Llamion-14B-LongChat
```
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 are detailed in our paper.

Notably, the LongChat model supports an extensive text range of 200K tokens. The following figure shows the perplexity of models on English Wikipedia corpus and Korean Wikipedia corpus, respectively.