Instructions to use Humachine/egypt-constitution-vlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Humachine/egypt-constitution-vlm with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-3-4b-it-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "Humachine/egypt-constitution-vlm")

Transformers

How to use Humachine/egypt-constitution-vlm with Transformers:

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

pipe = pipeline("text-generation", model="Humachine/egypt-constitution-vlm")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Humachine/egypt-constitution-vlm", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Humachine/egypt-constitution-vlm with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Humachine/egypt-constitution-vlm

SGLang

How to use Humachine/egypt-constitution-vlm 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 "Humachine/egypt-constitution-vlm" \
    --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": "Humachine/egypt-constitution-vlm",
		"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 "Humachine/egypt-constitution-vlm" \
        --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": "Humachine/egypt-constitution-vlm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use Humachine/egypt-constitution-vlm 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 Humachine/egypt-constitution-vlm 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 Humachine/egypt-constitution-vlm to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Humachine/egypt-constitution-vlm to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Humachine/egypt-constitution-vlm",
    max_seq_length=2048,
)

Docker Model Runner
How to use Humachine/egypt-constitution-vlm with Docker Model Runner:
```
docker model run hf.co/Humachine/egypt-constitution-vlm
```

Humachine commited on Apr 19

Commit

f86fbdd

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1 Parent(s): 4bf3e50

Update README.md

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@@ -116,9 +116,9 @@ messages = [
 ### Generate Output
 ```python
-inputs = tokenizer.apply_chat_template(
-    messages, add_generation_prompt=True, return_dict=True, return_tensors="pt"
-).to("cuda",dtype=torch.bfloat16)
 output_tokens = model.generate(**inputs, max_new_tokens=2048, use_cache=True, temperature=0.2)
 output_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)

 ### Generate Output
 ```python
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to("cuda",dtype=torch.bfloat16)
 output_tokens = model.generate(**inputs, max_new_tokens=2048, use_cache=True, temperature=0.2)
 output_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)