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koushikkb12
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Model Details
Model Provider
koushikkb12
Model Tree
Qwen/Qwen2.5-32B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Qwen/Qwen2.5-32B-InstructThis model excels at breaking down complex word problems and physics/math scenarios into step-by-step logical deductions. It is highly resistant to "hallucinating" math, as it was trained to self-correct during the generation phase.
Formatting Requirement: When prompting the model, instruct it to wrap its final answer in <answer> tags. It has been highly conditioned to follow this structural constraint.
Use the code below to dynamically load this LoRA adapter over the base Qwen 32B model.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel # Load the native 16-bit base modelbase_model_id = "Qwen/Qwen2.5-32B-Instruct"tokenizer = AutoTokenizer.from_pretrained(base_model_id) model = AutoModelForCausalLM.from_pretrained( base_model_id, torch_dtype=torch.bfloat16, device_map="auto", attn_implementation="sdpa",) # Attach the GRPO-trained LoRA adapteradapter_id = "koushikkb12/Qwen2.5-32B-GRPO-Math"model = PeftModel.from_pretrained(model, adapter_id) # Inferenceprompt = "A train leaves New York at 8:00 AM traveling 60 mph. Another leaves at 9:00 AM at 80 mph. When do they meet?"messages = [ {"role": "system", "content": "You are a logical math assistant. Wrap your final answer in <answer> tags."}, {"role": "user", "content": prompt}] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(text, return_tensors="pt").to("cuda") with torch.no_grad(): outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7) print(tokenizer.decode(outputs[0], skip_special_tokens=True))Trained on the GSM8K (Grade School Math 8K) dataset, evaluating against thousands of diverse algebraic and logical reasoning problems.
bfloat16 (Native 16-bit, NO 4-bit quantization used)peft: 0.19.1trl: Latesttransformers: Latesttorch: 2.8.0+cu128import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel # Load the native 16-bit base modelbase_model_id = "Qwen/Qwen2.5-32B-Instruct"tokenizer = AutoTokenizer.from_pretrained(base_model_id) model = AutoModelForCausalLM.from_pretrained( base_model_id, torch_dtype=torch.bfloat16, device_map="auto", attn_implementation="sdpa",) # Attach the GRPO-trained LoRA adapteradapter_id = "koushikkb12/Qwen2.5-32B-GRPO-Math"model = PeftModel.from_pretrained(model, adapter_id) # Inferenceprompt = "A train leaves New York at 8:00 AM traveling 60 mph. Another leaves at 9:00 AM at 80 mph. When do they meet?"messages = [ {"role": "system", "content": "You are a logical math assistant. Wrap your final answer in <answer> tags."}, {"role": "user", "content": prompt}] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(text, return_tensors="pt").to("cuda") with torch.no_grad(): outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7) print(tokenizer.decode(outputs[0], skip_special_tokens=True))