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Nenque-MoT-0.6B-Elite14 is a compact, high-efficiency model tailored for mathematical reasoning, code generation, and structured technical inference. Fine-tuned from Qwen3-0.6B using the MoT (Mixture of Thoughts) dataset—with a focus on math expert clusters—this model delivers strong symbolic performance in low-resource environments. Despite its 0.6B parameter size, it offers elite-level precision across STEM and multilingual technical domains.
1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model_name = "prithivMLmods/Nenque-MoT-0.6B-Elite14"
4
5model = AutoModelForCausalLM.from_pretrained(
6 model_name,
7 torch_dtype="auto",
8 device_map="auto"
9)
10tokenizer = AutoTokenizer.from_pretrained(model_name)
11
12prompt = "Solve the equation: 2(x - 4) + 3 = 11. Show all steps."
13
14messages = [
15 {"role": "system", "content": "You are a step-by-step math tutor."},
16 {"role": "user", "content": prompt}
17]
18
19text = tokenizer.apply_chat_template(
20 messages,
21 tokenize=False,
22 add_generation_prompt=True
23)
24
25model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
26
27generated_ids = model.generate(
28 **model_inputs,
29 max_new_tokens=512
30)
31generated_ids = [
32 output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
33]
34
35response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
36print(response)