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| Parameter | Value |
|---|---|
| Base Model | fdtn-ai/Foundation-Sec-8B |
| Training Samples | ~50,000 |
| Epochs | 2 |
| LoRA Rank | 16 |
| LoRA Alpha | 32 |
| Learning Rate | 2e-4 |
| Max Sequence Length | 1024 |
| Target Modules | 7 (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj) |
1from transformers import AutoModelForCausalLM, AutoTokenizer
2import torch
3
4model = AutoModelForCausalLM.from_pretrained(
5 "sainikhiljuluri2015/Foundation-Sec-Cybersecurity-8B-Merged",
6 torch_dtype=torch.bfloat16,
7 device_map="auto",
8 trust_remote_code=True
9)
10tokenizer = AutoTokenizer.from_pretrained("sainikhiljuluri2015/Foundation-Sec-Cybersecurity-8B-Merged", trust_remote_code=True)
11
12prompt = "What are the indicators of a ransomware attack?"
13inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
14outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
15print(tokenizer.decode(outputs[0], skip_special_tokens=True))1import requests
2
3API_URL = "https://YOUR_ENDPOINT_URL/v1/chat/completions"
4
5response = requests.post(API_URL, json={
6 "model": "sainikhiljuluri2015/Foundation-Sec-Cybersecurity-8B-Merged",
7 "messages": [{"role": "user", "content": "What is SQL injection?"}],
8 "max_tokens": 300
9})
10print(response.json()["choices"][0]["message"]["content"])