Instructions to use dhilipsiva/dhilipsiva-twin-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use dhilipsiva/dhilipsiva-twin-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dhilipsiva/dhilipsiva-twin-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf dhilipsiva/dhilipsiva-twin-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dhilipsiva/dhilipsiva-twin-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf dhilipsiva/dhilipsiva-twin-gguf:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf dhilipsiva/dhilipsiva-twin-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf dhilipsiva/dhilipsiva-twin-gguf:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf dhilipsiva/dhilipsiva-twin-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dhilipsiva/dhilipsiva-twin-gguf:Q8_0

Use Docker

docker model run hf.co/dhilipsiva/dhilipsiva-twin-gguf:Q8_0

LM Studio
Jan
Ollama
How to use dhilipsiva/dhilipsiva-twin-gguf with Ollama:
```
ollama run hf.co/dhilipsiva/dhilipsiva-twin-gguf:Q8_0
```

Unsloth Studio

How to use dhilipsiva/dhilipsiva-twin-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for dhilipsiva/dhilipsiva-twin-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for dhilipsiva/dhilipsiva-twin-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dhilipsiva/dhilipsiva-twin-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use dhilipsiva/dhilipsiva-twin-gguf with Docker Model Runner:
```
docker model run hf.co/dhilipsiva/dhilipsiva-twin-gguf:Q8_0
```

Lemonade

How to use dhilipsiva/dhilipsiva-twin-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dhilipsiva/dhilipsiva-twin-gguf:Q8_0

Run and chat with the model

lemonade run user.dhilipsiva-twin-gguf-Q8_0

List all available models

lemonade list

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.

Downloads last month: 145

GGUF

Model size

0.1B params

Architecture

llama

Hardware compatibility

8-bit

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Model tree for dhilipsiva/dhilipsiva-twin-gguf

Base model

HuggingFaceTB/SmolLM2-135M

Quantized

HuggingFaceTB/SmolLM2-135M-Instruct

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