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

Libraries

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

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

pipe = pipeline("text-generation", model="ali-thowfeek/Charty-1B")
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")
model = AutoModelForCausalLM.from_pretrained("ali-thowfeek/Charty-1B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

Unsloth Studio

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

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="ali-thowfeek/Charty-1B",
    max_seq_length=2048,
)

Docker Model Runner
How to use ali-thowfeek/Charty-1B with Docker Model Runner:
```
docker model run hf.co/ali-thowfeek/Charty-1B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Charty-1B

A compact text-to-Mermaid diagram generation model fine-tuned from Liquid AI's LFM2.5-1.2B-Instruct, built for on-device and mobile deployment. Give it a natural-language description and Charty-1B outputs raw Mermaid syntax only — no explanations, no markdown fences, no conversational filler.

This repository contains the merged full model in 16-bit safetensors format. For quantized GGUF versions (F16 / Q8 / Q4K_M), see ali-thowfeek/Charty-1B-GGUF

🗒️ 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, inherited from the base repo.

The same template is embedded in the GGUF builds (see the GGUF repo) 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

📦 GGUF / Quantized Versions

For CPU-only inference, mobile deployment, or use with llama.cpp / Ollama / LM Studio:

👉 ali-thowfeek/Charty-1B-GGUF

Quantization	Use Case
F16	Maximum quality, larger memory footprint
Q8_0	Near-lossless quality, balanced size
Q4KM	Smallest size, best for mobile / edge

📊 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}
}