Instructions to use vilm/VinaLlama2-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vilm/VinaLlama2-14B with Transformers:

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

pipe = pipeline("text-generation", model="vilm/VinaLlama2-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vilm/VinaLlama2-14B")
model = AutoModelForCausalLM.from_pretrained("vilm/VinaLlama2-14B")
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 vilm/VinaLlama2-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vilm/VinaLlama2-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vilm/VinaLlama2-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vilm/VinaLlama2-14B

SGLang

How to use vilm/VinaLlama2-14B 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 "vilm/VinaLlama2-14B" \
    --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": "vilm/VinaLlama2-14B",
		"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 "vilm/VinaLlama2-14B" \
        --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": "vilm/VinaLlama2-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use vilm/VinaLlama2-14B with Docker Model Runner:
```
docker model run hf.co/vilm/VinaLlama2-14B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

VinaLlama2-14B Beta

GGUF Here: VinaLlama2-14B-GGUF

Top Features:

Context Length: 32,768 tokens.
VERY GOOD at reasoning, mathematics and creative writing.
Works with Langchain Agent out-of-the-box.

Known Issues

Still a bit struggling with Vietnamese fact (Hoang Sa & Truong Sa, Historical questions).
Hallucination when reasoning.
Can't do Vi-En/En-Vi translation (yet)!

Quick use:

VRAM Requirement: ~20GB

pip install transformers accelerate

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "vilm/VinaLlama2-14B",
    torch_dtype='auto',
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("vilm/VinaLlama2-14B")

prompt = "Một cộng một bằng mấy?"
messages = [
    {"role": "system", "content": "Bạn là trợ lí AI hữu ích."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=1024,
    eos_token_id=tokenizer.eos_token_id,
    temperature=0.25,
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids)[0]
print(response)

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Safetensors

Model size

14B params

Tensor type

BF16

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Quantizations

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