Instructions to use ali-thowfeek/Charty-1B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ali-thowfeek/Charty-1B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="ali-thowfeek/Charty-1B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ali-thowfeek/Charty-1B-GGUF")
model = AutoModelForCausalLM.from_pretrained("ali-thowfeek/Charty-1B-GGUF", device_map="auto")

llama-cpp-python

How to use ali-thowfeek/Charty-1B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ali-thowfeek/Charty-1B-GGUF",
	filename="Chart-1B-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ali-thowfeek/Charty-1B-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 ali-thowfeek/Charty-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ali-thowfeek/Charty-1B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ali-thowfeek/Charty-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ali-thowfeek/Charty-1B-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 ali-thowfeek/Charty-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ali-thowfeek/Charty-1B-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 ali-thowfeek/Charty-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ali-thowfeek/Charty-1B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ali-thowfeek/Charty-1B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ali-thowfeek/Charty-1B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ali-thowfeek/Charty-1B-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": "ali-thowfeek/Charty-1B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ali-thowfeek/Charty-1B-GGUF:Q4_K_M

SGLang

How to use ali-thowfeek/Charty-1B-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 "ali-thowfeek/Charty-1B-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": "ali-thowfeek/Charty-1B-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 "ali-thowfeek/Charty-1B-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": "ali-thowfeek/Charty-1B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ali-thowfeek/Charty-1B-GGUF with Ollama:
```
ollama run hf.co/ali-thowfeek/Charty-1B-GGUF:Q4_K_M
```

Unsloth Studio

How to use ali-thowfeek/Charty-1B-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 ali-thowfeek/Charty-1B-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 ali-thowfeek/Charty-1B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ali-thowfeek/Charty-1B-GGUF to start chatting

How to use ali-thowfeek/Charty-1B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ali-thowfeek/Charty-1B-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": "ali-thowfeek/Charty-1B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ali-thowfeek/Charty-1B-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 ali-thowfeek/Charty-1B-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 ali-thowfeek/Charty-1B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ali-thowfeek/Charty-1B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ali-thowfeek/Charty-1B-GGUF:Q4_K_M

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 "ali-thowfeek/Charty-1B-GGUF:Q4_K_M" \
  --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 ali-thowfeek/Charty-1B-GGUF with Docker Model Runner:
```
docker model run hf.co/ali-thowfeek/Charty-1B-GGUF:Q4_K_M
```

Lemonade

How to use ali-thowfeek/Charty-1B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ali-thowfeek/Charty-1B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Charty-1B-GGUF-Q4_K_M

List all available models

lemonade list

Charty-1B — GGUF

Quantized GGUF versions of Charty-1B, a compact text-to-Mermaid diagram generation model fine-tuned from LFM2.5-1.2B-Instruct. Designed for on-device, mobile, and CPU-only inference.

For the original 16-bit safetensors, see ali-thowfeek/Charty-1B.

📦 Available Quantizations

File	Quantization	Size (approx.)	Quality	Best For
`Charty-1B-F16.gguf`	F16 (full)	~2.3 GB	★★★★★ Maximum	GPU inference, maximum fidelity
`Charty-1B-Q8_0.gguf`	Q8_0	~1.2 GB	★★★★☆ Near-lossless	Balanced CPU / edge deployment
`Charty-1B-Q4_K_M.gguf`	Q4_K_M	~0.7 GB	★★★☆☆ Good	Mobile phones, low-RAM devices

🗒️ Model Details

Field	Value
Base model	unsloth/LFM2.5-1.2B-Instruct
Architecture	LFM2 (hybrid: 10 double-gated LIV convolution blocks + 6 GQA attention blocks)
Parameters	1.17B
Context length	32,768 tokens
Vocabulary size	65,536
Fine-tuning method	LoRA (SFT) via Unsloth + Hugging Face TRL
Training dataset	ali-thowfeek/text-to-mermaid (3,837 examples)
Developed by	ali-thowfeek

💬 Chat Template (Unsloth's Fixed Template)

This model ships with Unsloth's fixed Jinja chat template embedded in the GGUF versions and is applied automatically by llama.cpp with --jinja.

🎯 Intended Use

Generate Mermaid diagram syntax (flowcharts, sequence, class, state, ER, Gantt, pie, git graphs, mindmaps, quadrant charts, …) from plain-text prompts. Run on low-end hardware: mobile phones, edge devices, laptops, embedded systems. Power apps that need offline, private, diagram-as-code generation.

What it does

Input (user prompt):

Create a flowchart showing the user login process with MFA verification.

Output (model response):

graph TD
    A[User Visits Login Page] --> B[Enter Credentials]
    B --> C{Credentials Valid?}
    C -->|No| D[Show Error Message]
    D --> B
    C -->|Yes| E[Send MFA Code]
    E --> F[Enter MFA Code]
    F --> G{MFA Valid?}
    G -->|No| H[Show MFA Error]
    H --> F
    G -->|Yes| I[Grant Access]

The model outputs only the Mermaid syntax code. No wrapping text, no mermaid fences.

🏃 Inference

Recommended Generation Parameters

Parameter	Value
temperature	0.1
top_k	50
top_p	0.1
repetition_penalty	1.05

📊 Training Details

Detail	Value
Framework	Unsloth + Hugging Face TRL
Method	LoRA Supervised Fine-Tuning (SFT), merged into full weights
Dataset	ali-thowfeek/text-to-mermaid
Dataset size	3,837 examples
Dataset source	Derived from Celiadraw/text-to-mermaid-2, cleaned, reworded, and validated against Mermaid v11 (core)
Validation	100% of training examples produce valid Mermaid v11 core syntax
Output format	Raw Mermaid syntax only (no markdown fences, no explanations)

📜 License

This model is a derivative work of LFM2.5-1.2B-Instruct by Liquid AI and is released under the LFM Open License v1.0.

Key terms:

✅ Free for research, personal, and non-commercial use.
✅ Commercial use permitted for entities with < $10M annual revenue.
❌ Commercial use by entities with ≥ $10M annual revenue is not licensed.
You must include a copy of the LICENSE with any redistribution.
You must retain all copyright and attribution notices.

See the full LICENSE file in this repository for complete terms.

📚 Citation

If you use this model, please cite the base model and this work:

@article{liquidai2025lfm2,
  title   = {LFM2 Technical Report},
  author  = {Liquid AI},
  journal = {arXiv preprint arXiv:2511.23404},
  year    = {2025}
}

@misc{thowfeek2026charty,
  title  = {Charty-1B: A Text-to-Mermaid Diagram Generation Model},
  author = {ali-thowfeek},
  year   = {2026},
  url    = {https://huggingface.co/ali-thowfeek/Charty-1B}
}