---

license: apache-2.0

language: [en]

base_model:

• HuggingFaceTB/SmolLM2-135M-Instruct
• Qwen/Qwen2.5-0.5B-Instruct

tags: [gguf, persona, candle, webassembly, chatml]

---

dhilipsiva-twin — on-device persona models

LoRA fine-tunes that impersonate dhilipsiva — they ARE his

website: served into the visitor's browser and run entirely on-device via

candle compiled to WebAssembly.

| file | base | size | extra trick |

|---|---|---|---|

| dhilipsiva-twin-q8_0.gguf | SmolLM2-135M-Instruct | 145MB | persona |

| dhilipsiva-twin-qwen-q8_0.gguf | Qwen2.5-0.5B-Instruct | 531MB | persona + emits TOOL {"app":…} lines that open the site's MCP apps |

Tokenizers included as tokenizer-smol.json / tokenizer-qwen.json.

ChatML prompting. The system prompt must match the training prompt verbatim —

see finetune/generate_dataset.py in the site repo

(SYSTEM for smol, SYSTEM_TOOLS for qwen). Low-temperature decoding recommended

(temp ~0.3): they answer as dhilipsiva on questions about him, and answer general

questions plainly in his voice — fit with a contrast corpus so they no longer recite

his bio for every prompt.

⊥ These models will lie, confidently. Fluent ≠ true — that gap is the point:

it's why dhilipsiva builds nibli, a

hallucination firewall that derives answers with proof traces instead of

predicting plausible text. Trained facts are accurate as of 2026-06; everything

else is improv.