Image-Text-to-Text
Transformers
Safetensors
PyTorch
English
Arabic
multilingual
zaqeris_zaqion
text-generation
multimodal
vision
150b
moe-150b
150b-moe
mixture-of-experts
48-experts
150-billion-parameters
large-model
large-language-model
causal-lm
vllm
code
math
reasoning
arabic
egypt
zaqeris
m4-gold
lattice-quantization
conversational
custom_code
Eval Results (legacy)
zaqeris_m4_gold
Instructions to use ZAQERIS/ZAQION with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZAQERIS/ZAQION with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ZAQERIS/ZAQION", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ZAQERIS/ZAQION", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ZAQERIS/ZAQION with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ZAQERIS/ZAQION" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ZAQERIS/ZAQION", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/ZAQERIS/ZAQION
- SGLang
How to use ZAQERIS/ZAQION 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 "ZAQERIS/ZAQION" \ --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": "ZAQERIS/ZAQION", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "ZAQERIS/ZAQION" \ --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": "ZAQERIS/ZAQION", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use ZAQERIS/ZAQION with Docker Model Runner:
docker model run hf.co/ZAQERIS/ZAQION
Commit History
Today
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Update pipeline tag to image-text-to-text and add vision/multimodal readiness tags
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Fix chat_template identity to ZAQION 1.0 (ZAQERIS AI) and update tokenizer class
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Purge sovereign/flagship/frontier references in evaluation_evidence/EVALUATION_METHODOLOGY_AND_EVIDENCE.md to ensure 100% academic compliance
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Purge sovereign/flagship/frontier references in PROVENANCE_AND_AUDIT.md to ensure 100% academic compliance
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Provide dedicated standalone vLLM layer implementation (ZaqionDecoderLayer, ZaqionAttention, ZaqionMoE)
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Implement full 64-layer Transformer MoE architecture with GQA and physical SwiGLU routing
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