Instructions to use ysn-rfd/calme-3.3-instruct-3b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ysn-rfd/calme-3.3-instruct-3b-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ysn-rfd/calme-3.3-instruct-3b-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ysn-rfd/calme-3.3-instruct-3b-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ysn-rfd/calme-3.3-instruct-3b-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 ysn-rfd/calme-3.3-instruct-3b-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf ysn-rfd/calme-3.3-instruct-3b-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ysn-rfd/calme-3.3-instruct-3b-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf ysn-rfd/calme-3.3-instruct-3b-GGUF:Q4_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 ysn-rfd/calme-3.3-instruct-3b-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf ysn-rfd/calme-3.3-instruct-3b-GGUF:Q4_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 ysn-rfd/calme-3.3-instruct-3b-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ysn-rfd/calme-3.3-instruct-3b-GGUF:Q4_0

Use Docker

docker model run hf.co/ysn-rfd/calme-3.3-instruct-3b-GGUF:Q4_0

LM Studio
Jan

vLLM

How to use ysn-rfd/calme-3.3-instruct-3b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ysn-rfd/calme-3.3-instruct-3b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ysn-rfd/calme-3.3-instruct-3b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ysn-rfd/calme-3.3-instruct-3b-GGUF:Q4_0

SGLang

How to use ysn-rfd/calme-3.3-instruct-3b-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ysn-rfd/calme-3.3-instruct-3b-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ysn-rfd/calme-3.3-instruct-3b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ysn-rfd/calme-3.3-instruct-3b-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ysn-rfd/calme-3.3-instruct-3b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ysn-rfd/calme-3.3-instruct-3b-GGUF with Ollama:
```
ollama run hf.co/ysn-rfd/calme-3.3-instruct-3b-GGUF:Q4_0
```

Unsloth Studio

How to use ysn-rfd/calme-3.3-instruct-3b-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 ysn-rfd/calme-3.3-instruct-3b-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 ysn-rfd/calme-3.3-instruct-3b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ysn-rfd/calme-3.3-instruct-3b-GGUF to start chatting

How to use ysn-rfd/calme-3.3-instruct-3b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ysn-rfd/calme-3.3-instruct-3b-GGUF:Q4_0

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": "ysn-rfd/calme-3.3-instruct-3b-GGUF:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ysn-rfd/calme-3.3-instruct-3b-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 ysn-rfd/calme-3.3-instruct-3b-GGUF:Q4_0

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 ysn-rfd/calme-3.3-instruct-3b-GGUF:Q4_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ysn-rfd/calme-3.3-instruct-3b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ysn-rfd/calme-3.3-instruct-3b-GGUF:Q4_0

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 "ysn-rfd/calme-3.3-instruct-3b-GGUF:Q4_0" \
  --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"

Docker Model Runner
How to use ysn-rfd/calme-3.3-instruct-3b-GGUF with Docker Model Runner:
```
docker model run hf.co/ysn-rfd/calme-3.3-instruct-3b-GGUF:Q4_0
```

Lemonade

How to use ysn-rfd/calme-3.3-instruct-3b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ysn-rfd/calme-3.3-instruct-3b-GGUF:Q4_0

Run and chat with the model

lemonade run user.calme-3.3-instruct-3b-GGUF-Q4_0

List all available models

lemonade list

calme-3.3-instruct-3b-GGUF / README.md

ysn-rfd

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	---
	base_model: MaziyarPanahi/calme-3.3-instruct-3b
	datasets:
	- MaziyarPanahi/french_instruct_sharegpt
	- arcee-ai/EvolKit-20k
	language:
	- fr
	- en
	library_name: transformers
	license: other
	license_name: qwen-research
	license_link: https://huggingface.co/Qwen/Qwen2.5-3B/blob/main/LICENSE
	pipeline_tag: text-generation
	tags:
	- chat
	- qwen
	- qwen2.5
	- finetune
	- french
	- english
	- llama-cpp
	- matrixportal
	inference: false
	model_creator: MaziyarPanahi
	quantized_by: MaziyarPanahi
	model-index:
	- name: calme-3.3-instruct-3b
	results:
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: IFEval (0-Shot)
	type: HuggingFaceH4/ifeval
	args:
	num_few_shot: 0
	metrics:
	- type: inst_level_strict_acc and prompt_level_strict_acc
	value: 64.23
	name: strict accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-3.3-instruct-3b
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: BBH (3-Shot)
	type: BBH
	args:
	num_few_shot: 3
	metrics:
	- type: acc_norm
	value: 25.68
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-3.3-instruct-3b
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MATH Lvl 5 (4-Shot)
	type: hendrycks/competition_math
	args:
	num_few_shot: 4
	metrics:
	- type: exact_match
	value: 0.0
	name: exact match
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-3.3-instruct-3b
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: GPQA (0-shot)
	type: Idavidrein/gpqa
	args:
	num_few_shot: 0
	metrics:
	- type: acc_norm
	value: 4.36
	name: acc_norm
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-3.3-instruct-3b
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MuSR (0-shot)
	type: TAUR-Lab/MuSR
	args:
	num_few_shot: 0
	metrics:
	- type: acc_norm
	value: 9.4
	name: acc_norm
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-3.3-instruct-3b
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU-PRO (5-shot)
	type: TIGER-Lab/MMLU-Pro
	config: main
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 25.62
	name: accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-3.3-instruct-3b
	name: Open LLM Leaderboard
	---

	# ysn-rfd/calme-3.3-instruct-3b-GGUF
	This model was converted to GGUF format from [`MaziyarPanahi/calme-3.3-instruct-3b`](https://huggingface.co/MaziyarPanahi/calme-3.3-instruct-3b) using llama.cpp via the ggml.ai's [all-gguf-same-where](https://huggingface.co/spaces/matrixportal/all-gguf-same-where) space.
	Refer to the [original model card](https://huggingface.co/MaziyarPanahi/calme-3.3-instruct-3b) for more details on the model.

	## ✅ Quantized Models Download List

	### 🔍 Recommended Quantizations
	- ✨ General CPU Use: [`Q4_K_M`](https://huggingface.co/ysn-rfd/calme-3.3-instruct-3b-GGUF/resolve/main/calme-3.3-instruct-3b-q4_k_m.gguf) (Best balance of speed/quality)
	- 📱 ARM Devices: [`Q4_0`](https://huggingface.co/ysn-rfd/calme-3.3-instruct-3b-GGUF/resolve/main/calme-3.3-instruct-3b-q4_0.gguf) (Optimized for ARM CPUs)
	- 🏆 Maximum Quality: [`Q8_0`](https://huggingface.co/ysn-rfd/calme-3.3-instruct-3b-GGUF/resolve/main/calme-3.3-instruct-3b-q8_0.gguf) (Near-original quality)

	### 📦 Full Quantization Options
	\| 🚀 Download \| 🔢 Type \| 📝 Notes \|
	\|:---------\|:-----\|:------\|
	\| [Download](https://huggingface.co/ysn-rfd/calme-3.3-instruct-3b-GGUF/resolve/main/calme-3.3-instruct-3b-q2_k.gguf) \| ![Q2_K](https://img.shields.io/badge/Q2_K-1A73E8) \| Basic quantization \|
	\| [Download](https://huggingface.co/ysn-rfd/calme-3.3-instruct-3b-GGUF/resolve/main/calme-3.3-instruct-3b-q3_k_s.gguf) \| ![Q3_K_S](https://img.shields.io/badge/Q3_K_S-34A853) \| Small size \|
	\| [Download](https://huggingface.co/ysn-rfd/calme-3.3-instruct-3b-GGUF/resolve/main/calme-3.3-instruct-3b-q3_k_m.gguf) \| ![Q3_K_M](https://img.shields.io/badge/Q3_K_M-FBBC05) \| Balanced quality \|
	\| [Download](https://huggingface.co/ysn-rfd/calme-3.3-instruct-3b-GGUF/resolve/main/calme-3.3-instruct-3b-q3_k_l.gguf) \| ![Q3_K_L](https://img.shields.io/badge/Q3_K_L-4285F4) \| Better quality \|
	\| [Download](https://huggingface.co/ysn-rfd/calme-3.3-instruct-3b-GGUF/resolve/main/calme-3.3-instruct-3b-q4_0.gguf) \| ![Q4_0](https://img.shields.io/badge/Q4_0-EA4335) \| Fast on ARM \|
	\| [Download](https://huggingface.co/ysn-rfd/calme-3.3-instruct-3b-GGUF/resolve/main/calme-3.3-instruct-3b-q4_k_s.gguf) \| ![Q4_K_S](https://img.shields.io/badge/Q4_K_S-673AB7) \| Fast, recommended \|
	\| [Download](https://huggingface.co/ysn-rfd/calme-3.3-instruct-3b-GGUF/resolve/main/calme-3.3-instruct-3b-q4_k_m.gguf) \| ![Q4_K_M](https://img.shields.io/badge/Q4_K_M-673AB7) ⭐ \| Best balance \|
	\| [Download](https://huggingface.co/ysn-rfd/calme-3.3-instruct-3b-GGUF/resolve/main/calme-3.3-instruct-3b-q5_0.gguf) \| ![Q5_0](https://img.shields.io/badge/Q5_0-FF6D01) \| Good quality \|
	\| [Download](https://huggingface.co/ysn-rfd/calme-3.3-instruct-3b-GGUF/resolve/main/calme-3.3-instruct-3b-q5_k_s.gguf) \| ![Q5_K_S](https://img.shields.io/badge/Q5_K_S-0F9D58) \| Balanced \|
	\| [Download](https://huggingface.co/ysn-rfd/calme-3.3-instruct-3b-GGUF/resolve/main/calme-3.3-instruct-3b-q5_k_m.gguf) \| ![Q5_K_M](https://img.shields.io/badge/Q5_K_M-0F9D58) \| High quality \|
	\| [Download](https://huggingface.co/ysn-rfd/calme-3.3-instruct-3b-GGUF/resolve/main/calme-3.3-instruct-3b-q6_k.gguf) \| ![Q6_K](https://img.shields.io/badge/Q6_K-4285F4) 🏆 \| Very good quality \|
	\| [Download](https://huggingface.co/ysn-rfd/calme-3.3-instruct-3b-GGUF/resolve/main/calme-3.3-instruct-3b-q8_0.gguf) \| ![Q8_0](https://img.shields.io/badge/Q8_0-EA4335) ⚡ \| Fast, best quality \|
	\| [Download](https://huggingface.co/ysn-rfd/calme-3.3-instruct-3b-GGUF/resolve/main/calme-3.3-instruct-3b-f16.gguf) \| ![F16](https://img.shields.io/badge/F16-000000) \| Maximum accuracy \|

	💡 Tip: Use `F16` for maximum precision when quality is critical


	---
	# 🚀 Applications and Tools for Locally Quantized LLMs
	## 🖥️ Desktop Applications

	\| Application \| Description \| Download Link \|
	\|-----------------\|----------------------------------------------------------------------------------------------\|--------------------------------------------------------------------------------\|
	\| Llama.cpp \| A fast and efficient inference engine for GGUF models. \| [GitHub Repository](https://github.com/ggml-org/llama.cpp) \|
	\| Ollama \| A streamlined solution for running LLMs locally. \| [Website](https://ollama.com/) \|
	\| AnythingLLM \| An AI-powered knowledge management tool. \| [GitHub Repository](https://github.com/Mintplex-Labs/anything-llm) \|
	\| Open WebUI \| A user-friendly web interface for running local LLMs. \| [GitHub Repository](https://github.com/open-webui/open-webui) \|
	\| GPT4All \| A user-friendly desktop application supporting various LLMs, compatible with GGUF models. \| [GitHub Repository](https://github.com/nomic-ai/gpt4all) \|
	\| LM Studio \| A desktop application designed to run and manage local LLMs, supporting GGUF format. \| [Website](https://lmstudio.ai/) \|
	\| GPT4All Chat\| A chat application compatible with GGUF models for local, offline interactions. \| [GitHub Repository](https://github.com/nomic-ai/gpt4all) \|

	---

	## 📱 Mobile Applications

	\| Application \| Description \| Download Link \|
	\|-------------------\|----------------------------------------------------------------------------------------------\|--------------------------------------------------------------------------------\|
	\| ChatterUI \| A simple and lightweight LLM app for mobile devices. \| [GitHub Repository](https://github.com/Vali-98/ChatterUI) \|
	\| Maid \| Mobile Artificial Intelligence Distribution for running AI models on mobile devices. \| [GitHub Repository](https://github.com/Mobile-Artificial-Intelligence/maid) \|
	\| PocketPal AI \| A mobile AI assistant powered by local models. \| [GitHub Repository](https://github.com/a-ghorbani/pocketpal-ai) \|
	\| Layla \| A flexible platform for running various AI models on mobile devices. \| [Website](https://www.layla-network.ai/) \|

	---

	## 🎨 Image Generation Applications

	\| Application \| Description \| Download Link \|
	\|-------------------------------------\|----------------------------------------------------------------------------------------------\|--------------------------------------------------------------------------------\|
	\| Stable Diffusion \| An open-source AI model for generating images from text. \| [GitHub Repository](https://github.com/CompVis/stable-diffusion) \|
	\| Stable Diffusion WebUI \| A web application providing access to Stable Diffusion models via a browser interface. \| [GitHub Repository](https://github.com/AUTOMATIC1111/stable-diffusion-webui) \|
	\| Local Dream \| Android Stable Diffusion with Snapdragon NPU acceleration. Also supports CPU inference. \| [GitHub Repository](https://github.com/xororz/local-dream) \|
	\| Stable-Diffusion-Android (SDAI) \| An open-source AI art application for Android devices, enabling digital art creation. \| [GitHub Repository](https://github.com/ShiftHackZ/Stable-Diffusion-Android) \|

	---