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nehalkamal7
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Model Details
Model Provider
nehalkamal7
Model Tree
Qwen/Qwen2.5-Coder-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Qwen/Qwen2.5-Coder-1.5B-Instructbitsandbytes and peft)You can load this model along with its base weights using Hugging Face transformers and peft:
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base_model_id = "Qwen/Qwen2.5-Coder-1.5B-Instruct"adapter_id = "nehalkamal7/devguard-qwen-1.5b-security" # Load Tokenizertokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True) # Load 4-bit Base Modelmodel = AutoModelForCausalLM.from_pretrained( base_model_id, torch_dtype=torch.float16, device_map="auto") # Load DevGuard LoRA Adaptermodel = PeftModel.from_pretrained(model, adapter_id) # Perform Security Auditprompt = """<|im_start|>systemYou are DevGuard AI, an expert code security auditor.<|im_end|><|im_start|>userAudit this code and provide secure remediation:def execute_query(user_id, cursor): query = "SELECT * FROM users WHERE id = " + user_id cursor.execute(query)<|im_end|><|im_start|>assistant""" inputs = tokenizer(prompt, return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.2)print(tokenizer.decode(outputs[0], skip_special_tokens=True))import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base_model_id = "Qwen/Qwen2.5-Coder-1.5B-Instruct"adapter_id = "nehalkamal7/devguard-qwen-1.5b-security" # Load Tokenizertokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True) # Load 4-bit Base Modelmodel = AutoModelForCausalLM.from_pretrained( base_model_id, torch_dtype=torch.float16, device_map="auto") # Load DevGuard LoRA Adaptermodel = PeftModel.from_pretrained(model, adapter_id) # Perform Security Auditprompt = """<|im_start|>systemYou are DevGuard AI, an expert code security auditor.<|im_end|><|im_start|>userAudit this code and provide secure remediation:def execute_query(user_id, cursor): query = "SELECT * FROM users WHERE id = " + user_id cursor.execute(query)<|im_end|><|im_start|>assistant""" inputs = tokenizer(prompt, return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.2)print(tokenizer.decode(outputs[0], skip_special_tokens=True))