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ollama run technobyte/Qwen2.5-7B-VNTL-JP-EN:q4_k_mllama-cli -m Qwen2.5-7B-VNTL-JP-EN-Q4_K_M.gguf -no-cnv -p "A Japanese sentence along with a proper English equivalent.\nJapanese: 放課後はマンガ喫茶でまったり〜♡ おすすめのマンガ教えて! \nEnglish: "1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model_name = "TechnoByte/Qwen2.5-7B-VNTL-JP-EN"
4
5model = AutoModelForCausalLM.from_pretrained(
6 model_name,
7 torch_dtype="auto",
8 device_map="auto"
9)
10tokenizer = AutoTokenizer.from_pretrained(model_name)
11
12messages = [
13 {"role": "user", "content": "放課後はマンガ喫茶でまったり〜♡ おすすめのマンガ教えて!"}
14]
15text = tokenizer.apply_chat_template(
16 messages,
17 tokenize=False,
18 add_generation_prompt=True
19)
20model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
21
22generated_ids = model.generate(
23 **model_inputs,
24 max_new_tokens=512
25)
26generated_ids = [
27 output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
28]
29
30response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]A Japanese sentence along with a proper English equivalent.
Japanese: JAPANESE SENTENCE HERE
English: 1{% for i in range(0, messages|length, 2) %}A Japanese sentence along with a proper English equivalent.
2Japanese: {{ messages[i].content }}
3English:{% if i+1 < messages|length %} {{ messages[i+1].content }}<|endoftext|>{{ "
4" }}{% else %}{% endif %}{% endfor %}A Japanese sentence along with a proper English equivalent.
Japanese: {{ .Prompt }}
English: {{ .Response }}<|endoftext|>