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ehzawad
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Model Details
Model Provider
ehzawad
Model Tree
Base
Qwen/Qwen3-1.7B
Adapter
this model
Input Modalities
Text
Output Modalities
Text
Supported Functionality
Dedicated Endpoints
Container
You can use this model by loading the base model and applying this PEFT adapter. Here is a standalone code snippet:
!pip install --upgrade torchao !pip install -U bitsandbytes>=0.46.1
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModelbase_model_id = "Qwen/Qwen3-1.7B"adapter_id = "ehzawad/qwen3-1.7b-gsm8k-grpo"bnb_config = BitsAndBytesConfig(load_in_4bit=True,bnb_4bit_quant_type="nf4",bnb_4bit_compute_dtype=torch.bfloat16,)base_model = AutoModelForCausalLM.from_pretrained(base_model_id,quantization_config=bnb_config,device_map="auto",)tokenizer = AutoTokenizer.from_pretrained(adapter_id)hf_model = PeftModel.from_pretrained(base_model, adapter_id, is_trainable=False)hf_model.eval()prompt = "Janet has 3 bags with 4 apples each. She gives away 5 apples. How many remain?"system_prompt = ("You are a careful math reasoning assistant. ""Solve the problem step by step, but keep the solution concise. ""End with exactly one final answer in the form \boxed{answer}.")messages = [{"role": "system", "content": system_prompt},{"role": "user", "content": prompt}]inputs = tokenizer.apply_chat_template(messages,tokenize=True,add_generation_prompt=True,return_tensors="pt",return_dict=True).to(hf_model.device)with torch.inference_mode():outputs = hf_model.generate(**inputs,max_new_tokens=512,temperature=0.6,do_sample=True,)response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)print(response)