Instructions to use jsbeaudry/makandal-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jsbeaudry/makandal-v2 with Transformers:

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

pipe = pipeline("text-generation", model="jsbeaudry/makandal-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jsbeaudry/makandal-v2")
model = AutoModelForCausalLM.from_pretrained("jsbeaudry/makandal-v2")
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

vLLM

How to use jsbeaudry/makandal-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jsbeaudry/makandal-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jsbeaudry/makandal-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jsbeaudry/makandal-v2

SGLang

How to use jsbeaudry/makandal-v2 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 "jsbeaudry/makandal-v2" \
    --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": "jsbeaudry/makandal-v2",
		"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 "jsbeaudry/makandal-v2" \
        --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": "jsbeaudry/makandal-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jsbeaudry/makandal-v2 with Docker Model Runner:
```
docker model run hf.co/jsbeaudry/makandal-v2
```

Makandal Continue Pre-trained from qwen3-0.6b

Model Details

This model has been continued pre-trained from qwen3-0.6b by Palmis Labs AI. .

Model Description

Developed by: Palmis Labs AI
Funded by: Jean Sauvenel Beaudry
Model type: GPT (Generative Pre-trained Transformer)
Language(s) (NLP): Haitian Creole
License: MIT
Model size: 0.6B parameters
Architecture: qwen3

Direct Use

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

def generate(model, tokenizer, prompt, device):
    inputs = tokenizer(prompt, return_tensors="pt", padding=True).to(device)
    output = model.generate(
        **inputs,
        max_new_tokens=100,
        do_sample=True,
        repetition_penalty=1.2,
        no_repeat_ngram_size=3,
        temperature=0.9,
        top_k=40,
        top_p=0.85,
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=tokenizer.eos_token_id
    )
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("jsbeaudry/makandal-v2")
model = AutoModelForCausalLM.from_pretrained("jsbeaudry/makandal-v2")

# Set device
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# Generate text
prompt = "matematik"
response = generate(model, tokenizer, prompt, device)
print(response)

# Answer:
# Matematik se yon disiplin matematik ki konsantre sou kalkil, estatistik, ak analiz matematik.
# Li pèmèt nou konprann enfòmasyon ak fòmèlman analize done pou jwenn pwopriyete oswa fòmèlman verifye yon konpreyansyon.

Out-of-Scope Use

This model should NOT be used for:

Critical decision-making systems
Any application requiring reliable or factual outputs
Commercial deployment without significant additional training

Bias, Risks, and Limitations

Insufficient training data: Only 4.7 MB of training data used
Limited training time: Only 4.5 hours of training
High hallucination rate: Model frequently generates inaccurate or nonsensical content
Language coverage: Limited Haitian Creole language understanding due to minimal dataset
Bias: May reflect biases present in the small training dataset

Recommendations

Do not rely on outputs for factual information
Supervise usage in educational settings

Training Infrastructure

GPU: Tesla T4 (15GB)
Framework: Transformers/PyTorch

Citation

@misc{makandal2025,
  title={Makandal-pretrain: An Educational Haitian Creole Language Model},
  author={Jean Sauvenel Beaudry},
  year={2025},
  howpublished={\url{https://huggingface.co/jsbeaudry/makandal-pre-trained}},
  note={Educational demonstration model}
}

Glossary

Makandal: Named after François Makandal, an 18th-century Haitian revolutionary leader, symbolizing the model's connection to Haitian culture and education.

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Safetensors

Model size

0.6B params

Tensor type

BF16

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