Instructions to use arelath/Llama-3.2-1B-Instruct-nanoquant-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 arelath/Llama-3.2-1B-Instruct-nanoquant-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 arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF
# Run inference directly in the terminal:
llama cli -hf arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF
# Run inference directly in the terminal:
llama cli -hf arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF

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 arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF
# Run inference directly in the terminal:
./llama-cli -hf arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF

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 arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF

Use Docker

docker model run hf.co/arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF

LM Studio
Jan
Ollama
How to use arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF with Ollama:
```
ollama run hf.co/arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF
```

Unsloth Studio

How to use arelath/Llama-3.2-1B-Instruct-nanoquant-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 arelath/Llama-3.2-1B-Instruct-nanoquant-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 arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF to start chatting

How to use arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF with Docker Model Runner:
```
docker model run hf.co/arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF
```

Lemonade

How to use arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF

Run and chat with the model

lemonade run user.Llama-3.2-1B-Instruct-nanoquant-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "arelath/Llama-3.2-1B-Instruct-nanoquant-GGUF" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Experiment 25: meta-llama/Llama-3.2-1B-Instruct quality benchmark

Status: completed
Model: meta-llama/Llama-3.2-1B-Instruct
Revision: 9213176726f574b556790deb65791e0c5aa438b6
Candidate run: D:\dev\research\NanoQuantRewrite\evidence\025\025-compress-and-benchmark-llama-3-2-1b-instruct
Backend: dense
Wall time: 50.07 seconds

completed means all evaluators returned finite metrics; it is not a BF16-quality acceptance gate.

Protocol

WikiText-2: 64 samples × 128 tokens, batch 8
WikiText token hash: sha256:c7dc8810186996593a9a8a419db6bcae8e67e520c2412722425817fc5862a77d
Tasks: piqa, arc_easy, arc_challenge, hellaswag, winogrande, boolq; first 200 rows, batch 4
Tokenizer hash: sha256:5409af4b5ead403c8c413b60287460703373a37222ce25ce929035b49b81719c

Quality results

Benchmark	Metric	BF16	NanoQuant	Delta	Ratio
WikiText-2	perplexity ↓	36.856393	116.980145	+80.123753 (+217.39%)	3.1739x
piqa	acc_norm ↑	0.7350	0.6650	-0.0700	0.9048x
arc_easy	acc_norm ↑	0.6200	0.4250	-0.1950	0.6855x
arc_challenge	acc_norm ↑	0.3300	0.2950	-0.0350	0.8939x
hellaswag	acc_norm ↑	0.6050	0.4550	-0.1500	0.7521x
winogrande	acc ↑	0.6150	0.5550	-0.0600	0.9024x
boolq	acc ↑	0.7500	0.6450	-0.1050	0.8600x

Runtime and memory

Model	Elapsed seconds	Peak CUDA bytes	Peak host bytes
BF16	19.98	5,920,260,096	4,135,366,656
NanoQuant	18.02	6,192,889,856	4,869,566,464

Provenance

Experiment config hash: sha256:5be8cce6ef0ec17fcd90ecf10c7971523799070d4c61eeda245207a1ac69b319
Launcher: experiments/025-compress-and-benchmark-llama-3-2-1b-instruct.py
Candidate identity: {"config_hash":"sha256:a282bec0f20d082888d7322301dc95ef57f39a8b110317c9499aea8313acb3c4","model_hash":"sha256:0d4bbbbc32aa6ccf91325a258c47d5bf3839604149b6e081cb561c6a65e58f51","plan_hash":"sha256-3a9a46309740f9452a117627cb32f36f21f2fc9558d918aac3b601b5a0258da8"}
Global tuning: {"artifact_id":"sha256-5c4902d094ed2d025b63e3f24ba3b9b0ccca3e5bc0541da64bc6a3def1ca0b4a","artifact_type":"global-tuning-result","schema_version":1}

Downloads last month: 42

GGUF

Model size

0.3B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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