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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metadata

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
            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 using llama.cpp via the ggml.ai's all-gguf-same-where space. Refer to the original model card for more details on the model.

✅ Quantized Models Download List

🔍 Recommended Quantizations

✨ General CPU Use: Q4_K_M (Best balance of speed/quality)
📱 ARM Devices: Q4_0 (Optimized for ARM CPUs)
🏆 Maximum Quality: Q8_0 (Near-original quality)

📦 Full Quantization Options

🚀 Download	🔢 Type	📝 Notes
Download		Basic quantization
Download		Small size
Download		Balanced quality
Download		Better quality
Download		Fast on ARM
Download		Fast, recommended
Download	⭐	Best balance
Download		Good quality
Download		Balanced
Download		High quality
Download	🏆	Very good quality
Download	⚡	Fast, best quality
Download		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
Ollama	A streamlined solution for running LLMs locally.	Website
AnythingLLM	An AI-powered knowledge management tool.	GitHub Repository
Open WebUI	A user-friendly web interface for running local LLMs.	GitHub Repository
GPT4All	A user-friendly desktop application supporting various LLMs, compatible with GGUF models.	GitHub Repository
LM Studio	A desktop application designed to run and manage local LLMs, supporting GGUF format.	Website
GPT4All Chat	A chat application compatible with GGUF models for local, offline interactions.	GitHub Repository

📱 Mobile Applications

Application	Description	Download Link
ChatterUI	A simple and lightweight LLM app for mobile devices.	GitHub Repository
Maid	Mobile Artificial Intelligence Distribution for running AI models on mobile devices.	GitHub Repository
PocketPal AI	A mobile AI assistant powered by local models.	GitHub Repository
Layla	A flexible platform for running various AI models on mobile devices.	Website

🎨 Image Generation Applications

Application	Description	Download Link
Stable Diffusion	An open-source AI model for generating images from text.	GitHub Repository
Stable Diffusion WebUI	A web application providing access to Stable Diffusion models via a browser interface.	GitHub Repository
Local Dream	Android Stable Diffusion with Snapdragon NPU acceleration. Also supports CPU inference.	GitHub Repository
Stable-Diffusion-Android (SDAI)	An open-source AI art application for Android devices, enabling digital art creation.	GitHub Repository