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-thinking-100K-Q4_K_M-GGUF / README.md

x1nx3r

Upload README.md with huggingface_hub

ebae05e verified 14 days ago

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	---
	license: llama3.2
	datasets:
	- a-m-team/AM-DeepSeek-R1-Distilled-1.4M
	language:
	- en
	base_model:
	- meta-llama/Llama-3.2-3B
	---
	# 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
	```bash
	./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.