Text Generation
Transformers
Safetensors
English
Chinese
multilingual
qwen3_5
image-text-to-text
abliterated
uncensored
qwen3
qwen3.6
nvfp4
modelopt
mtp
multi-token-prediction
speculative-decoding
hybrid-attention
mamba
gated-deltanet
multimodal
aeon
rtx-5090
rtx-pro-6000
b100
b200
dedicated-vram-blackwell
sm_120
sm_100
32gb
conv1d-preserved
conversational
8-bit precision
Instructions to use connorhzp/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connorhzp/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="connorhzp/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS") 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)# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("connorhzp/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS") model = AutoModelForImageTextToText.from_pretrained("connorhzp/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS") 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?"} ] }, ] inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use connorhzp/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "connorhzp/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "connorhzp/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/connorhzp/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS
- SGLang
How to use connorhzp/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS 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 "connorhzp/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS" \ --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": "connorhzp/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS", "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 "connorhzp/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS" \ --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": "connorhzp/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use connorhzp/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS with Docker Model Runner:
docker model run hf.co/connorhzp/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS
| { | |
| "architectures": [ | |
| "Qwen3_5ForConditionalGeneration" | |
| ], | |
| "dtype": "bfloat16", | |
| "image_token_id": 248056, | |
| "language_model_only": false, | |
| "model_type": "qwen3_5", | |
| "text_config": { | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "attn_output_gate": true, | |
| "bos_token_id": 248044, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 248044, | |
| "full_attention_interval": 4, | |
| "head_dim": 256, | |
| "hidden_act": "silu", | |
| "hidden_size": 5120, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 17408, | |
| "layer_types": [ | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention" | |
| ], | |
| "linear_conv_kernel_dim": 4, | |
| "linear_key_head_dim": 128, | |
| "linear_num_key_heads": 16, | |
| "linear_num_value_heads": 48, | |
| "linear_value_head_dim": 128, | |
| "mamba_ssm_dtype": "float32", | |
| "max_position_embeddings": 262144, | |
| "model_type": "qwen3_5_text", | |
| "mtp_num_hidden_layers": 1, | |
| "mtp_use_dedicated_embeddings": false, | |
| "num_attention_heads": 24, | |
| "num_hidden_layers": 64, | |
| "num_key_value_heads": 4, | |
| "output_gate_type": "swish", | |
| "pad_token_id": null, | |
| "partial_rotary_factor": 0.25, | |
| "rms_norm_eps": 1e-06, | |
| "rope_parameters": { | |
| "mrope_interleaved": true, | |
| "mrope_section": [ | |
| 11, | |
| 11, | |
| 10 | |
| ], | |
| "partial_rotary_factor": 0.25, | |
| "rope_theta": 10000000, | |
| "rope_type": "default" | |
| }, | |
| "tie_word_embeddings": false, | |
| "use_cache": true, | |
| "vocab_size": 248320 | |
| }, | |
| "tie_word_embeddings": false, | |
| "transformers_version": "5.5.3", | |
| "video_token_id": 248057, | |
| "vision_config": { | |
| "deepstack_visual_indexes": [], | |
| "depth": 27, | |
| "dtype": "bfloat16", | |
| "hidden_act": "gelu_pytorch_tanh", | |
| "hidden_size": 1152, | |
| "in_channels": 3, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4304, | |
| "model_type": "qwen3_5", | |
| "num_heads": 16, | |
| "num_position_embeddings": 2304, | |
| "out_hidden_size": 5120, | |
| "patch_size": 16, | |
| "spatial_merge_size": 2, | |
| "temporal_patch_size": 2 | |
| }, | |
| "vision_end_token_id": 248054, | |
| "vision_start_token_id": 248053, | |
| "quantization_config": { | |
| "config_groups": { | |
| "group_0": { | |
| "input_activations": { | |
| "dynamic": false, | |
| "num_bits": 4, | |
| "type": "float", | |
| "group_size": 16 | |
| }, | |
| "weights": { | |
| "dynamic": false, | |
| "num_bits": 4, | |
| "type": "float", | |
| "group_size": 16 | |
| }, | |
| "targets": [ | |
| "Linear" | |
| ] | |
| } | |
| }, | |
| "ignore": [ | |
| "lm_head", | |
| "model.language_model.layers.0.linear_attn.conv1d", | |
| "model.language_model.layers.1.linear_attn.conv1d", | |
| "model.language_model.layers.10.linear_attn.conv1d", | |
| "model.language_model.layers.12.linear_attn.conv1d", | |
| "model.language_model.layers.13.linear_attn.conv1d", | |
| "model.language_model.layers.14.linear_attn.conv1d", | |
| "model.language_model.layers.16.linear_attn.conv1d", | |
| "model.language_model.layers.17.linear_attn.conv1d", | |
| "model.language_model.layers.18.linear_attn.conv1d", | |
| "model.language_model.layers.2.linear_attn.conv1d", | |
| "model.language_model.layers.20.linear_attn.conv1d", | |
| "model.language_model.layers.21.linear_attn.conv1d", | |
| "model.language_model.layers.22.linear_attn.conv1d", | |
| "model.language_model.layers.24.linear_attn.conv1d", | |
| "model.language_model.layers.25.linear_attn.conv1d", | |
| "model.language_model.layers.26.linear_attn.conv1d", | |
| "model.language_model.layers.28.linear_attn.conv1d", | |
| "model.language_model.layers.29.linear_attn.conv1d", | |
| "model.language_model.layers.30.linear_attn.conv1d", | |
| "model.language_model.layers.32.linear_attn.conv1d", | |
| "model.language_model.layers.33.linear_attn.conv1d", | |
| "model.language_model.layers.34.linear_attn.conv1d", | |
| "model.language_model.layers.36.linear_attn.conv1d", | |
| "model.language_model.layers.37.linear_attn.conv1d", | |
| "model.language_model.layers.38.linear_attn.conv1d", | |
| "model.language_model.layers.4.linear_attn.conv1d", | |
| "model.language_model.layers.40.linear_attn.conv1d", | |
| "model.language_model.layers.41.linear_attn.conv1d", | |
| "model.language_model.layers.42.linear_attn.conv1d", | |
| "model.language_model.layers.44.linear_attn.conv1d", | |
| "model.language_model.layers.45.linear_attn.conv1d", | |
| "model.language_model.layers.46.linear_attn.conv1d", | |
| "model.language_model.layers.48.linear_attn.conv1d", | |
| "model.language_model.layers.49.linear_attn.conv1d", | |
| "model.language_model.layers.5.linear_attn.conv1d", | |
| "model.language_model.layers.50.linear_attn.conv1d", | |
| "model.language_model.layers.52.linear_attn.conv1d", | |
| "model.language_model.layers.53.linear_attn.conv1d", | |
| "model.language_model.layers.54.linear_attn.conv1d", | |
| "model.language_model.layers.56.linear_attn.conv1d", | |
| "model.language_model.layers.57.linear_attn.conv1d", | |
| "model.language_model.layers.58.linear_attn.conv1d", | |
| "model.language_model.layers.6.linear_attn.conv1d", | |
| "model.language_model.layers.60.linear_attn.conv1d", | |
| "model.language_model.layers.61.linear_attn.conv1d", | |
| "model.language_model.layers.62.linear_attn.conv1d", | |
| "model.language_model.layers.8.linear_attn.conv1d", | |
| "model.language_model.layers.9.linear_attn.conv1d", | |
| "model.visual*", | |
| "mtp.fc", | |
| "mtp.layers.0.input_layernorm", | |
| "mtp.layers.0.mlp.down_proj", | |
| "mtp.layers.0.mlp.gate_proj", | |
| "mtp.layers.0.mlp.up_proj", | |
| "mtp.layers.0.post_attention_layernorm", | |
| "mtp.layers.0.self_attn.k_norm", | |
| "mtp.layers.0.self_attn.k_proj", | |
| "mtp.layers.0.self_attn.o_proj", | |
| "mtp.layers.0.self_attn.q_norm", | |
| "mtp.layers.0.self_attn.q_proj", | |
| "mtp.layers.0.self_attn.v_proj", | |
| "mtp.norm", | |
| "mtp.pre_fc_norm_embedding", | |
| "mtp.pre_fc_norm_hidden" | |
| ], | |
| "quant_algo": "NVFP4", | |
| "producer": { | |
| "name": "modelopt", | |
| "version": "0.43.0" | |
| }, | |
| "quant_method": "modelopt", | |
| "exclude_modules": [ | |
| "mtp.fc", | |
| "mtp.layers.0.input_layernorm", | |
| "mtp.layers.0.mlp.down_proj", | |
| "mtp.layers.0.mlp.gate_proj", | |
| "mtp.layers.0.mlp.up_proj", | |
| "mtp.layers.0.post_attention_layernorm", | |
| "mtp.layers.0.self_attn.k_norm", | |
| "mtp.layers.0.self_attn.k_proj", | |
| "mtp.layers.0.self_attn.o_proj", | |
| "mtp.layers.0.self_attn.q_norm", | |
| "mtp.layers.0.self_attn.q_proj", | |
| "mtp.layers.0.self_attn.v_proj", | |
| "mtp.norm", | |
| "mtp.pre_fc_norm_embedding", | |
| "mtp.pre_fc_norm_hidden" | |
| ] | |
| } | |
| } |