Instructions to use x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF",
	filename="checkpoint-11500-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 x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF: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 x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF: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 x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF:Q4_K_M
```

Unsloth Studio

How to use x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-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 x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-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 x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF to start chatting

How to use x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF:Q4_K_M

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": "x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF:Q4_K_M

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 x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull x1nx3r/Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.2-3B-thinking-100K-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

Llama 3.2 3B Reasoning Model

Model Details

Base Model: Meta Llama 3.2 3B Fine-tuning: Full-weight training on 100k DeepSeek R1 reasoning examples Training Infrastructure: MI300X with bf16 precision Context Length: 131,072 tokens Reasoning Format: Structured thinking with <think></think> tags

Usage

This repository contains the Q4_K_M GGUF version of the model, ready for use with Ollama or llama.cpp.

Sampling Parameters

./llama-cli -m checkpoint-11500-Q4_K_M.gguf \
  --temp 0.3 \
  --top-p 0.9 \
  --top-k 40 \
  --repeat-penalty 1.15 \
  -p "Your prompt here" \
  -n 1024

Expected Output Format

The model will structure its responses with reasoning tags:

<think>
Let me solve this step by step...
Speed = Distance / Time
Speed = 300km / 4 hours = 75 km/h
</think>
The average speed of the train is 75 km/h (kilometers per hour).

Model Capabilities

Strengths:

Mathematical reasoning and calculations
Step-by-step problem solving
Logical analysis and deduction
Code reasoning and debugging
Scientific problem solving

Limitations:

May generate verbose reasoning for simple questions
Occasional repetition in thinking process
Not trained for specific domain knowledge beyond general reasoning

License

This model is based on Llama 3.2 and follows Meta's licensing terms.

Downloads last month: 154

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

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