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Monster-Code
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Monster-Code
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Qwen/Qwen2.5-0.5B
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| Component | Detail |
|---|---|
| Base Model | Qwen/Qwen2.5-0.5B (494M params) |
| Adapter Type | PEFT LoRA (r=16, lora_alpha=32, target modules: q_proj, v_proj, k_proj, o_proj) |
| Custom Output Head | sev_head — Sequential MLP: D → D/2 → 10 (GELU + Dropout 0.1) |
| Context Length | 512 tokens |
| Max Candidates Per Pass | 10 |
| Total Parameters | ~510M (base + LoRA + head) |
| Inference Precision | bfloat16 on CUDA, float32 on CPU |
Sev-1-Nano transforms multi-candidate selection into a single forward pass. Rather than scoring each option independently (N forward passes), it pads all candidates into a structured prompt and uses the sev_head to emit 10 logits in parallel — each corresponding to one candidate's relative preference score.
text
Input: <state>\n<question>\n<opt1> | <opt2> | ... | <opt10>Output: 10-dimensional logit vector → argmax → best optionSev-1-Nano was evaluated on the PhishNChips benchmark, a large-scale phishing detection testbed spanning 11 frontier models and 10 system-prompt strategies (220,000 evaluations total). Our model achieves 58.67% core recall on the core benchmark at a 3.8% false-positive rate — competitive with much larger judge models.
Key insight: Sev-1-Nano achieves 58.67% recall with only 494M parameters — a fraction of models that reach similar performance. This demonstrates that specialized LoRA + linear-head fine-tuning can compress judge capabilities into an extremely lightweight package.
Reference benchmark scores (5-shot and 0-shot) from the Qwen2.5 Technical Report, showing how our base model compares to larger siblings:
From the SLMJury study comparing 16 SLM judges (0.6B–14B) across 10 benchmarks:
Sev-1-Nano operates as a local 10-way judge/reranker. Logits output by sev_head represent relative preferences among candidates present within the current input prompt.
Note: For multi-class classification tasks with >10 classes (e.g., Banking77, CLINC150), evaluate candidates using direct LLM log-likelihood scoring rather than 10-option chunked head inference.
Load the model using standard PyTorch, Hugging Face transformers, and peft:
python
import torchimport torch.nn as nnfrom transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModelfrom huggingface_hub import hf_hub_download MODEL_NAME = "Qwen/Qwen2.5-0.5B"REPO_ID = "Monster-Code/Sev-1-nano"MAX_OPTIONS = 10DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") class SevJudge(nn.Module): def __init__(self, base_model_name: str, max_options: int = 10): super().__init__() dtype = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float32 base = AutoModelForCausalLM.from_pretrained(base_model_name, torch_dtype=dtype) self.backbone = getattr(base, "model", base) hidden_dim = base.config.hidden_size self.sev_head = nn.Sequential( nn.Linear(hidden_dim, hidden_dim // 2), nn.GELU(), nn.Dropout(0.1), nn.Linear(hidden_dim // 2, max_options) ).to(dtype) def forward(self, input_ids, attention_mask): outputs = self.backbone(input_ids=input_ids, attention_mask=attention_mask) return self.sev_head(outputs.last_hidden_state[:, -1, :]) # 1. Load Tokenizer & Base Architecturetokenizer = AutoTokenizer.from_pretrained(REPO_ID)model = SevJudge(MODEL_NAME, max_options=MAX_OPTIONS) # 2. Attach LoRA Adaptermodel.backbone = PeftModel.from_pretrained(model.backbone, REPO_ID) # 3. Load Custom Linear Head Weightshead_path = hf_hub_download(repo_id=REPO_ID, filename="sev_head.pt")model.sev_head.load_state_dict(torch.load(head_path, map_location=DEVICE))model.to(DEVICE)model.eval() # 4. Inference Exampledef judge_options(text, options, state=""): num_items = len(options) padded_options = options + [options[0]] * (MAX_OPTIONS - num_items) prompt = f"State: {state}\nQuestion: {text}\nOptions: {', '.join(padded_options)}" tokens = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512).to(DEVICE) with torch.no_grad(): logits = model(tokens["input_ids"], tokens["attention_mask"])[0] valid_logits = logits[:num_items] best_idx = torch.argmax(valid_logits).item() return best_idx, options[best_idx] choices = ["Legitimate Email", "Phishing Attempt"]best_idx, best_choice = judge_options("Your account password has expired. Click here to reset.", choices)print(f"Selected Choice ({best_idx}): {best_choice}")SargeDev/jev-distill-corpus-v3 backbone alignment.| File | Description |
|---|---|
adapter_model.bin | LoRA adapter weights for the Qwen2.5-0.5B backbone |
sev_head.pt | Custom 10-dimensional output head (MLP: D → D/2 → 10) |
tokenizer files | Qwen2.5 tokenizer (vocabulary, merges, configs) |
README.md | This model card |
If you use Sev-1-Nano in your research, please cite:
bibtex
@misc{sev-1-nano, title = {Sev-1-Nano: Lightweight 10-Option Judge via LoRA}, author = {Monster-Code}, year = {2026}, url = {https://huggingface.co/Monster-Code/Sev-1-nano-0.5B}}import torchimport torch.nn as nnfrom transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModelfrom huggingface_hub import hf_hub_download MODEL_NAME = "Qwen/Qwen2.5-0.5B"REPO_ID = "Monster-Code/Sev-1-nano"MAX_OPTIONS = 10DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") class SevJudge(nn.Module): def __init__(self, base_model_name: str, max_options: int = 10): super().__init__() dtype = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float32 base = AutoModelForCausalLM.from_pretrained(base_model_name, torch_dtype=dtype) self.backbone = getattr(base, "model", base) hidden_dim = base.config.hidden_size self.sev_head = nn.Sequential( nn.Linear(hidden_dim, hidden_dim // 2), nn.GELU(), nn.Dropout(0.1), nn.Linear(hidden_dim // 2, max_options) ).to(dtype) def forward(self, input_ids, attention_mask): outputs = self.backbone(input_ids=input_ids, attention_mask=attention_mask) return self.sev_head(outputs.last_hidden_state[:, -1, :]) # 1. Load Tokenizer & Base Architecturetokenizer = AutoTokenizer.from_pretrained(REPO_ID)model = SevJudge(MODEL_NAME, max_options=MAX_OPTIONS) # 2. Attach LoRA Adaptermodel.backbone = PeftModel.from_pretrained(model.backbone, REPO_ID) # 3. Load Custom Linear Head Weightshead_path = hf_hub_download(repo_id=REPO_ID, filename="sev_head.pt")model.sev_head.load_state_dict(torch.load(head_path, map_location=DEVICE))model.to(DEVICE)model.eval() # 4. Inference Exampledef judge_options(text, options, state=""): num_items = len(options) padded_options = options + [options[0]] * (MAX_OPTIONS - num_items) prompt = f"State: {state}\nQuestion: {text}\nOptions: {', '.join(padded_options)}" tokens = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512).to(DEVICE) with torch.no_grad(): logits = model(tokens["input_ids"], tokens["attention_mask"])[0] valid_logits = logits[:num_items] best_idx = torch.argmax(valid_logits).item() return best_idx, options[best_idx] choices = ["Legitimate Email", "Phishing Attempt"]best_idx, best_choice = judge_options("Your account password has expired. Click here to reset.", choices)print(f"Selected Choice ({best_idx}): {best_choice}")@misc{sev-1-nano, title = {Sev-1-Nano: Lightweight 10-Option Judge via LoRA}, author = {Monster-Code}, year = {2026}, url = {https://huggingface.co/Monster-Code/Sev-1-nano-0.5B}}