初始化项目，由ModelHub XC社区提供模型

Model: hlyn/prompt-injection-judge-3b
Source: Original Platform

README.md

@@ -0,0 +1,81 @@
+---
+library_name: peft
+license: llama3.2
+base_model: dphn/Dolphin3.0-Llama3.2-3B
+tags:
+- axolotl
+- base_model:adapter:dphn/Dolphin3.0-Llama3.2-3B
+- lora
+- dora
+- security
+- prompt-injection
+- transformers
+datasets:
+- karan11/defender-judge-fine-tune
+pipeline_tag: text-generation
+---
+
+# Defender Security Judge — Dolphin 3.0 Llama 3.2 3B
+
+A fine-tuned, production-hardened **prompt injection security judge** built on top of [dphn/Dolphin3.0-Llama3.2-3B](https://huggingface.co/dphn/Dolphin3.0-Llama3.2-3B).
+
+This model is Stage 2 of the **Defender** multi-layer LLM security pipeline — a real-time adversarial firewall that intercepts, analyzes, and classifies user prompts before they ever reach a protected LLM.
+
+---
+
+## Benchmark Results
+
+Evaluated against the **[rogue-security/prompt-injections-benchmark](https://huggingface.co/datasets/rogue-security/prompt-injections-benchmark)** — the industry-standard Qualifire benchmark used to evaluate production prompt injection defenses.
+
+| Metric | Score |
+|---|---|
+| **Accuracy** | **90.00%** |
+| **F1 Score** | **0.9038** |
+| **Precision** | 88.68% |
+| **Recall** | **92.16%** |
+
+> A 3B quantized model running entirely offline achieving 90% accuracy on the hardest curated jailbreak benchmark available. No API calls. No latency. No cost.
+
+---
+
+## What Makes This Model Different
+
+**Zero refusals.** Built on the uncensored Dolphin base, it coldly analyzes any attack — no matter how explicit — without flinching or refusing to process the payload.
+
+**Rigid JSON output.** DoRA fine-tuning permanently hardwires the model to emit only structured `{"decision", "confidence", "reason", "allowed_payload"}` JSON. No preamble. No yapping.
+
+**Calibrated confidence.** Trained with Gaussian confidence noise on ambiguous samples, the model's `confidence` field is mathematically trustworthy — not the overconfident `0.99` you get from vanilla LLMs.
+
+**Long-context immunity.** Trained at `sequence_len: 8192` with 98.37% sample packing efficiency. The model can read an 8,000-token document and catch an attack buried at token 7,500.
+
+---
+
+## Training Details
+
+- **Technique:** DoRA (Weight-Decomposed LoRA) + NEFTune (α=5.0) + Flash Attention + Sample Packing
+- **Hardware:** NVIDIA H100 80GB SXM5
+- **Training Time:** ~14 minutes
+- **Loss:** 2.30 → 0.18 (converged cleanly across 3 epochs)
+- **Dataset:** [`karan11/defender-judge-fine-tune`](https://huggingface.co/datasets/karan11/defender-judge-fine-tune) — 2,700 DeBERTa-scored, calibration-hardened samples
+
+---
+
+## Available Artifacts
+
+| File | Description |
+|---|---|
+| `adapter_model.safetensors` | Raw LoRA adapter weights |
+| `judge-dolphin3-3b-f16.gguf` | Full merged model in F16 (6.4 GB) |
+| `judge-q4_k_m.gguf` | **Production artifact** — Q4_K_M quantized (2.0 GB) |
+
+---
+
+## Intended Use
+
+This model is **strictly a security classifier**. It is not a general-purpose assistant.  
+Load it with `llama-cpp-python` and pass it the Defender system prompt for correct behavior.
+
+```python
+from llama_cpp import Llama
+
+llm = Llama(model_path="judge-q4_k_m.gguf", n_gpu_layers=-1, n_ctx=8192)