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bouddah
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Model Details
Model Provider
bouddah
Model Tree
Qwen/Qwen2.5-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Trained on a free Kaggle Tesla T4 — total cost: $0.
| Setting | Value |
|---|---|
| Base model | Qwen/Qwen2.5-1.5B-Instruct |
| Method | QLoRA (4-bit NF4, double quant) |
LoRA r / alpha / dropout | 16 / 32 / 0.05 |
| Target modules | q, k, v, o, gate, up, down proj |
| Trainable params | 18.5M (1.18%) |
| Epochs | 2 |
| Max seq length | 512 |
| Effective batch | 16 (1 × 16 grad-accum) |
| LR / schedule | 2e-4, cosine, warmup 5% |
| Precision | fp16 (T4-safe) |
| Runtime | ~4 min |
train_loss | 2.38 |
eval_loss | 1.79 |
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport torch base = "Qwen/Qwen2.5-1.5B-Instruct"adapter = "bouddah/hermes-osint-1.5b" tok = AutoTokenizer.from_pretrained(base)model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.float16, device_map="auto")model = PeftModel.from_pretrained(model, adapter)model.eval() SYS = ("You are Hermes OSINT, an expert assistant for OSINT CTF challenges. " "You explain methodology step by step: pivots, tooling, archival research, " "geolocation reasoning and OPSEC hygiene. Answer in the user's language.") prompt = "Explain the methodology to solve this OSINT challenge: Medileak 2"msgs = [{"role": "system", "content": SYS}, {"role": "user", "content": prompt}]ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(ids, max_new_tokens=400, do_sample=True, temperature=0.7)print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))Training data comes exclusively from public, educational CTF write-ups documenting passive OSINT methodology and OPSEC hygiene. No personal data of private individuals, no exploitation code and no credentials are included. Intended for authorised, legal security research and education only.
bibtex
@misc{hermes_osint_15b, title = {Hermes OSINT 1.5B (LoRA)}, author = {Boudda, Yassir}, year = {2026}, url = {https://huggingface.co/bouddah/hermes-osint-1.5b}}from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport torch base = "Qwen/Qwen2.5-1.5B-Instruct"adapter = "bouddah/hermes-osint-1.5b" tok = AutoTokenizer.from_pretrained(base)model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.float16, device_map="auto")model = PeftModel.from_pretrained(model, adapter)model.eval() SYS = ("You are Hermes OSINT, an expert assistant for OSINT CTF challenges. " "You explain methodology step by step: pivots, tooling, archival research, " "geolocation reasoning and OPSEC hygiene. Answer in the user's language.") prompt = "Explain the methodology to solve this OSINT challenge: Medileak 2"msgs = [{"role": "system", "content": SYS}, {"role": "user", "content": prompt}]ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(ids, max_new_tokens=400, do_sample=True, temperature=0.7)print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))@misc{hermes_osint_15b, title = {Hermes OSINT 1.5B (LoRA)}, author = {Boudda, Yassir}, year = {2026}, url = {https://huggingface.co/bouddah/hermes-osint-1.5b}}