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sainikhiljuluri2015
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Model Details
Model Provider
sainikhiljuluri2015
Model Tree
openai/gpt-oss-20b
Input Modalities
Output Modalities
Supported Functionality
GPT-OSS-20B is a Mixture of Experts (MoE) model with efficient inference.
This model was trained on ~50,000 cybersecurity instruction-response pairs from:
| Parameter | Value |
|---|---|
| Base Model | openai/gpt-oss-20b |
| Architecture | MoE (21B total, 3.6B active) |
| Training Samples | ~50,000 |
| Epochs | 2 |
| LoRA Rank | 16 |
| LoRA Alpha | 32 |
| Learning Rate | 2e-4 |
| Max Sequence Length | 1024 |
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model = AutoModelForCausalLM.from_pretrained( "sainikhiljuluri2015/GPT-OSS-Cybersecurity-20B-Merged", torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)tokenizer = AutoTokenizer.from_pretrained("sainikhiljuluri2015/GPT-OSS-Cybersecurity-20B-Merged", trust_remote_code=True) prompt = "What are the indicators of a ransomware attack?"inputs = tokenizer(prompt, return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)print(tokenizer.decode(outputs[0], skip_special_tokens=True))python
import requests API_URL = "https://YOUR_ENDPOINT_URL/v1/chat/completions" response = requests.post(API_URL, json={ "model": "sainikhiljuluri2015/GPT-OSS-Cybersecurity-20B-Merged", "messages": [{"role": "user", "content": "What is SQL injection?"}], "max_tokens": 300})print(response.json()["choices"][0]["message"]["content"])Due to the 21B parameter size (MoE), recommended:
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model = AutoModelForCausalLM.from_pretrained( "sainikhiljuluri2015/GPT-OSS-Cybersecurity-20B-Merged", torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)tokenizer = AutoTokenizer.from_pretrained("sainikhiljuluri2015/GPT-OSS-Cybersecurity-20B-Merged", trust_remote_code=True) prompt = "What are the indicators of a ransomware attack?"inputs = tokenizer(prompt, return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)print(tokenizer.decode(outputs[0], skip_special_tokens=True))import requests API_URL = "https://YOUR_ENDPOINT_URL/v1/chat/completions" response = requests.post(API_URL, json={ "model": "sainikhiljuluri2015/GPT-OSS-Cybersecurity-20B-Merged", "messages": [{"role": "user", "content": "What is SQL injection?"}], "max_tokens": 300})print(response.json()["choices"][0]["message"]["content"])