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1from transformers import AutoTokenizer, AutoModelForCausalLM
2import torch
3
4# Load model and tokenizer
5model_name = "anhtuan15082023/gemma-3n-vneid-merged"
6tokenizer = AutoTokenizer.from_pretrained(model_name)
7model = AutoModelForCausalLM.from_pretrained(
8 model_name,
9 torch_dtype=torch.float16,
10 device_map="auto",
11 trust_remote_code=True
12)
13
14# Generate Vietnamese text
15def generate_vietnamese_text(prompt, max_length=100):
16 inputs = tokenizer(prompt, return_tensors="pt")
17
18 with torch.no_grad():
19 outputs = model.generate(
20 **inputs,
21 max_length=max_length,
22 temperature=0.7,
23 do_sample=True,
24 top_p=0.9,
25 pad_token_id=tokenizer.eos_token_id
26 )
27
28 response = tokenizer.decode(outputs[0], skip_special_tokens=True)
29 return response[len(prompt):].strip()
30
31# Example usage
32prompt = "Xin chào, tôi là"
33result = generate_vietnamese_text(prompt)
34print(f"Input: {prompt}")
35print(f"Output: {result}")1import requests
2
3API_URL = "https://api-inference.huggingface.co/models/anhtuan15082023/gemma-3n-vneid-merged"
4headers = {"Authorization": f"Bearer {YOUR_HF_TOKEN}"}
5
6def query(payload):
7 response = requests.post(API_URL, headers=headers, json=payload)
8 return response.json()
9
10# Generate text
11output = query({
12 "inputs": "Việt Nam là",
13 "parameters": {
14 "max_length": 100,
15 "temperature": 0.7
16 }
17})
18print(output)1@model{vietnamese-gemma-finetuned,
2 title={Vietnamese Fine-tuned Gemma Model},
3 author={anhtuan15082023},
4 year={2024},
5 url={https://huggingface.co/anhtuan15082023/gemma-3n-vneid-merged}
6}