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Ushitha
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Model Details
Model Provider
Ushitha
Model Tree
Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Give it an insecure or AI-generated Cisco config — it will:
python
from transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModelimport torch base = "Qwen/Qwen2.5-7B-Instruct"lora_repo = "Ushitha/ushitha-coder-network-corrector" tokenizer = AutoTokenizer.from_pretrained(base)model = AutoModelForCausalLM.from_pretrained( base, torch_dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(model, lora_repo) insecure_config = """hostname Routerinterface GigabitEthernet0/0 ip address 192.168.1.1 255.255.255.0 no shutdown""" messages = [ {"role": "system", "content": "You are a network security expert..."}, {"role": "user", "content": f"Review this config:\n{insecure_config}"},]text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(text, return_tensors="pt").to(model.device)output = model.generate(**inputs, max_new_tokens=2048, temperature=0.1, do_sample=True)print(tokenizer.decode(output[0], skip_special_tokens=True))| Parameter | Value |
|---|---|
| Base model | Qwen/Qwen2.5-7B-Instruct |
| Technique | QLoRA 4-bit NF4 |
| LoRA rank / alpha | 16 / 32 |
| Training examples | 246 |
| Epochs | 3 |
| Effective batch size | 8 |
| Learning rate | 0.0002 |
| Hardware | NVIDIA A40 48 GB |
from transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModelimport torch base = "Qwen/Qwen2.5-7B-Instruct"lora_repo = "Ushitha/ushitha-coder-network-corrector" tokenizer = AutoTokenizer.from_pretrained(base)model = AutoModelForCausalLM.from_pretrained( base, torch_dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(model, lora_repo) insecure_config = """hostname Routerinterface GigabitEthernet0/0 ip address 192.168.1.1 255.255.255.0 no shutdown""" messages = [ {"role": "system", "content": "You are a network security expert..."}, {"role": "user", "content": f"Review this config:\n{insecure_config}"},]text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(text, return_tensors="pt").to(model.device)output = model.generate(**inputs, max_new_tokens=2048, temperature=0.1, do_sample=True)print(tokenizer.decode(output[0], skip_special_tokens=True))