5CD-AI/Vietnamese-meta-math-MetaMathQA-40K-gg-translated
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How to use vankha/vietnamese-phi-4-reasoning with Transformers:
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("vankha/vietnamese-phi-4-reasoning", dtype="auto")How to use vankha/vietnamese-phi-4-reasoning with Unsloth Studio:
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 vankha/vietnamese-phi-4-reasoning to start chatting
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 vankha/vietnamese-phi-4-reasoning to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for vankha/vietnamese-phi-4-reasoning to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="vankha/vietnamese-phi-4-reasoning",
max_seq_length=2048,
)This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
!pip install --no-deps xformers="trl<0.9.0" peft accelerate bitsandbytes
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "vankha/vietnamese-phi-4-reasoning",
max_seq_length = 2048,
load_in_4bit = True,
)
messages = [
{
"role": "user",
"content": "Allen và Ben đang sơn hàng rào. Tỷ lệ giữa số lượng công việc Allen làm và số lượng công việc Ben làm là $3:5$. Nếu hàng rào cần sơn tổng cộng X feet vuông thì Ben sơn 150 feet vuông. Giá trị của biến X chưa biết là bao nhiêu?"
}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True, # Required for the model to generate responses
enable_thinking=True, # Can enable or disable "think" feature
)
from transformers import TextStreamer
_ = model.generate(
**tokenizer(text, return_tensors="pt").to("cuda"),
max_new_tokens=1024,
temperature=0.6,
top_p=0.95,
top_k=20,
streamer=TextStreamer(tokenizer, skip_prompt=True),
)