Feynman-Grpo-Exp is based on the Qwen 0.5B modality architecture, designed to enhance the reasoning capabilities of 0.5B-parameter models. It has been fine-tuned using the GRPO trainer on the OpenAI GSM8K dataset for reinforcement learning, improving its ability to handle complex reasoning tasks, multi-step problem-solving, and mathematical challenges. This model excels in chain-of-thought (CoT) reasoning and logical problem-solving, making it suitable for a variety of advanced tasks that require precise and structured outputs.
1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model_name = "prithivMLmods/Feynman-Grpo-Exp"
4
5model = AutoModelForCausalLM.from_pretrained(
6 model_name,
7 torch_dtype="auto",
8 device_map="auto",
9 trust_remote_code=True
10)
11tokenizer = AutoTokenizer.from_pretrained(model_name)
12
13prompt = "Give me a short introduction to large language models."
14messages = [
15 {"role": "system", "content": "You are an advanced AI assistant with expert-level reasoning and knowledge."},
16 {"role": "user", "content": prompt}
17]
18text = tokenizer.apply_chat_template(
19 messages,
20 tokenize=False,
21 add_generation_prompt=True
22)
23model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
24
25generated_ids = model.generate(
26 **model_inputs,
27 max_new_tokens=512
28)
29generated_ids = [
30 output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
31]
32
33response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
34print(response)
