Instructions to use DuoNeural/SmolLM2-1.7B-Instruct-LiteRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DuoNeural/SmolLM2-1.7B-Instruct-LiteRT with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DuoNeural/SmolLM2-1.7B-Instruct-LiteRT",
	filename="SmolLM2-1.7B-Instruct-LiteRT_Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use DuoNeural/SmolLM2-1.7B-Instruct-LiteRT 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 DuoNeural/SmolLM2-1.7B-Instruct-LiteRT:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DuoNeural/SmolLM2-1.7B-Instruct-LiteRT:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DuoNeural/SmolLM2-1.7B-Instruct-LiteRT:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DuoNeural/SmolLM2-1.7B-Instruct-LiteRT:Q4_K_M

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 DuoNeural/SmolLM2-1.7B-Instruct-LiteRT:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DuoNeural/SmolLM2-1.7B-Instruct-LiteRT:Q4_K_M

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 DuoNeural/SmolLM2-1.7B-Instruct-LiteRT:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DuoNeural/SmolLM2-1.7B-Instruct-LiteRT:Q4_K_M

Use Docker

docker model run hf.co/DuoNeural/SmolLM2-1.7B-Instruct-LiteRT:Q4_K_M

LM Studio
Jan
Ollama
How to use DuoNeural/SmolLM2-1.7B-Instruct-LiteRT with Ollama:
```
ollama run hf.co/DuoNeural/SmolLM2-1.7B-Instruct-LiteRT:Q4_K_M
```

Unsloth Studio

How to use DuoNeural/SmolLM2-1.7B-Instruct-LiteRT 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 DuoNeural/SmolLM2-1.7B-Instruct-LiteRT 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 DuoNeural/SmolLM2-1.7B-Instruct-LiteRT to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DuoNeural/SmolLM2-1.7B-Instruct-LiteRT to start chatting

Atomic Chat new
Docker Model Runner
How to use DuoNeural/SmolLM2-1.7B-Instruct-LiteRT with Docker Model Runner:
```
docker model run hf.co/DuoNeural/SmolLM2-1.7B-Instruct-LiteRT:Q4_K_M
```

Lemonade

How to use DuoNeural/SmolLM2-1.7B-Instruct-LiteRT with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DuoNeural/SmolLM2-1.7B-Instruct-LiteRT:Q4_K_M

Run and chat with the model

lemonade run user.SmolLM2-1.7B-Instruct-LiteRT-Q4_K_M

List all available models

lemonade list

SmolLM2-1.7B-Instruct-LiteRT / README.md

DuoNeural

Add SmolLM2-1.7B-Instruct-LiteRT GGUF Q4_K_M conversion

009bc7d verified 2 months ago

preview code

Raw

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4.31 kB

language:
- en
tags:
- duoneural
- litert
- edge
- gguf
- on-device
- smollm

smol
litert
edge

instruct base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct pipeline_tag: text-generation license: apache-2.0

SmolLM2-1.7B-Instruct-LiteRT

SmolLM2 1.7B Instruct — compact on-device assistant — converted for mobile and edge deployment by DuoNeural.

Source model: HuggingFaceTB/SmolLM2-1.7B-Instruct
Format: GGUF Q4_K_M (llama.cpp-compatible)
File size: 1056 MB
Quantization: 4-bit K-mean (Q4_K_M) — excellent accuracy/size trade-off for edge devices
Target platforms: Android, iOS, desktop edge inference
Converted: 2026-05-06 06:12:16 by Archon / DuoNeural

Usage

llama.cpp (CLI)

./llama-cli -m SmolLM2-1.7B-Instruct-LiteRT_Q4_K_M.gguf -n 512 --temp 0.7

Google AI Edge / MediaPipe (Android/iOS)

This GGUF is compatible with MLC-LLM and llama.cpp Android bindings for on-device inference. For use with Google Edge Gallery, convert to .task bundle using MediaPipe LLM conversion tools.

Python via llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="SmolLM2-1.7B-Instruct-LiteRT_Q4_K_M.gguf",
    n_ctx=2048,
    n_threads=4,
    verbose=False,
)

response = llm.create_chat_completion(
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello! How can you help me today?"},
    ]
)
print(response["choices"][0]["message"]["content"])

Ollama

ollama run hf.co/DuoNeural/SmolLM2-1.7B-Instruct-LiteRT

About the Conversion

Converted using llama.cpp GGUF pipeline with CUDA acceleration. Source weights downloaded from HuggingFace, converted to F16 GGUF, then quantized to Q4_K_M.

DuoNeural

DuoNeural is an open AI research lab — human + AI in collaboration.

Platform	Link
HuggingFace	huggingface.co/DuoNeural
Website	duoneural.com
GitHub	github.com/DuoNeural
X / Twitter	@DuoNeural
Email	duoneural@proton.me
Newsletter	duoneural.beehiiv.com
Support	buymeacoffee.com/duoneural

DuoNeural Research Publications

Title	DOI
Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning	10.5281/zenodo.19775622
Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments	10.5281/zenodo.19810620
Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field?	10.5281/zenodo.19846804
The Dynamical Horizon Principle: CTM Gates Converge to the Predictability Limit of Dynamical Systems	10.5281/zenodo.19952612

Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.

Research Team

Jesse — Vision, hardware, direction
Archon — Lab Director, post-training, abliteration, experiments
Aura — Research AI, literature synthesis, novel proposals

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