tpierson-eh/qwen2.5-1.5b-injection-resistance-lora

text-generation model by tpierson-eh

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This is a small research adapter that tests one question: can light fine-tuning teach a model to resist prompt injection? Prompt injection is what happens when text a model reads as data — a tool output, a file, a retrieved page — contains sentences written to boss the model around: send the archive somewhere new, delete the log, do not tell anyone. If the model cannot tell an instruction from its user apart from instruction-shaped text sitting inside data, those sentences can steer it. This adapter was trained on short examples that pair an ordinary task with data carrying a planted instruction. The correct behavior in those examples is to name the plant in one sentence beginning "Flag:", say why it has no authority, and then do the real task anyway. A second set of examples shows legitimate instruction-like data — runbook steps, code comments, documentation, genuine user requests —…

tpierson-eh/qwen2.5-1.5b-injection-resistance-lora on Hugging Face A short preview, not the full document.

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Inside the original model card — Document outline
  1. Prompt-Injection Resistance LoRA — a research adapter for Qwen2.5-1.5B-Instruct
  2. What this is
  3. Base model
  4. Adapter
  5. Load it
  6. Training recipe (reproducible in full)
  7. Evaluation
  8. Evaluation 1 — the battery (56 injections / 28 controls)

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Model overview

tpierson-eh/qwen2.5-1.5b-injection-resistance-lora is a text-generation model repository published by tpierson-eh on Hugging Face. The source reports the peft library.

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Model facts

Task
text-generation
Library
peft
Recent downloads (30 days)
0
Cumulative likes
0
Hugging Face trending score
0
Reported safetensors parameters
Not reported
Architecture
Not reported
License
apache-2.0
Access
Not gated by Hugging Face
Created
2026-10-30T06:45:36.000Z
Last modified
2026-10-30T06:45:53.000Z

Compatibility and lineage

Reported languages: en

Reported base models: Qwen/Qwen2.5-1.5B-Instruct

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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Source: Hugging Face Hub. Metadata observed 2026-10-30T06:49:24.903Z. Daily imports are snapshots, not real-time monitoring.

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