Instructions to use thangvip/vwen-1.5B-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thangvip/vwen-1.5B-instruct with Transformers:

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

pipe = pipeline("text-generation", model="thangvip/vwen-1.5B-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("thangvip/vwen-1.5B-instruct")
model = AutoModelForCausalLM.from_pretrained("thangvip/vwen-1.5B-instruct")
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 thangvip/vwen-1.5B-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thangvip/vwen-1.5B-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thangvip/vwen-1.5B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thangvip/vwen-1.5B-instruct

SGLang

How to use thangvip/vwen-1.5B-instruct 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 "thangvip/vwen-1.5B-instruct" \
    --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": "thangvip/vwen-1.5B-instruct",
		"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 "thangvip/vwen-1.5B-instruct" \
        --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": "thangvip/vwen-1.5B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use thangvip/vwen-1.5B-instruct with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for thangvip/vwen-1.5B-instruct to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for thangvip/vwen-1.5B-instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for thangvip/vwen-1.5B-instruct to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="thangvip/vwen-1.5B-instruct",
    max_seq_length=2048,
)

Docker Model Runner
How to use thangvip/vwen-1.5B-instruct with Docker Model Runner:
```
docker model run hf.co/thangvip/vwen-1.5B-instruct
```

Model Card for Model ID

LoRA SFT model Qwen2-1.5B on 600k sample of OpenHermes vietnamese (translated)

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: thangvip
Model type: Qwen2
Language(s) (NLP): Vietnamese
Finetuned from model: Qwen/Qwen2-1.5B

Bias, Risks, and Limitations

Have not trained on RLHF for safety

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("thangvip/vwen-1.5B-instruct", device_map="auto", cache_dir="./cache").eval()
tokenizer = AutoTokenizer.from_pretrained("thangvip/vwen-1.5B-instruct", cache_dir="./cache")

messages = [
  {'role': 'system', 'content': "bạn là trợ lý AI hữu ích"},
  {"role": "user", "content": "Nước nào có diện tích lớn nhất?"},
]
text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = tokenizer(text, return_tensors="pt")
inputs = {k: v.to("cuda") for k, v in inputs.items()}

outputs = model.generate(**inputs, tokenizer=tokenizer, max_new_tokens=256, do_sample=True, top_p=0.95, temperature=0.1, repetition_penalty=1.2, eos_token_id=tokenizer.eos_token_id, stop_strings=['<|im_end|>'])
print(tokenizer.decode(outputs[0]))

Training Details

Training Data

Trained on OpenHermes dataset (translated to Vietnamese) > 600k samples

Training Hyperparameters

Target_modules: q_proj, k_proj, v_proj, o_proj, up_proj, down_proj, gate_proj
batch_size: 2048
epoch: 1
optimizer: paged_adamw_8bit
warmup_ratio: 0.02
learning_rate: 1e-5

Downloads last month: 3

Safetensors

Model size

2B params

Tensor type

F32

thangvip
/

vwen-1.5B-instruct