Image-Text-to-Text
Transformers
Safetensors
MLX
qwen3_5_moe
open4bits
conversational
4-bit precision
Instructions to use Open4bits/Qwen3.6-35B-A3B-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Open4bits/Qwen3.6-35B-A3B-mlx-4Bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Open4bits/Qwen3.6-35B-A3B-mlx-4Bit") 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("Open4bits/Qwen3.6-35B-A3B-mlx-4Bit") model = AutoModelForImageTextToText.from_pretrained("Open4bits/Qwen3.6-35B-A3B-mlx-4Bit") 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]:])) - MLX
How to use Open4bits/Qwen3.6-35B-A3B-mlx-4Bit with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("Open4bits/Qwen3.6-35B-A3B-mlx-4Bit") config = load_config("Open4bits/Qwen3.6-35B-A3B-mlx-4Bit") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- vLLM
How to use Open4bits/Qwen3.6-35B-A3B-mlx-4Bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Open4bits/Qwen3.6-35B-A3B-mlx-4Bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Open4bits/Qwen3.6-35B-A3B-mlx-4Bit", "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/Open4bits/Qwen3.6-35B-A3B-mlx-4Bit
- SGLang
How to use Open4bits/Qwen3.6-35B-A3B-mlx-4Bit 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 "Open4bits/Qwen3.6-35B-A3B-mlx-4Bit" \ --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": "Open4bits/Qwen3.6-35B-A3B-mlx-4Bit", "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 "Open4bits/Qwen3.6-35B-A3B-mlx-4Bit" \ --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": "Open4bits/Qwen3.6-35B-A3B-mlx-4Bit", "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" } } ] } ] }' - Pi
How to use Open4bits/Qwen3.6-35B-A3B-mlx-4Bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Open4bits/Qwen3.6-35B-A3B-mlx-4Bit"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Open4bits/Qwen3.6-35B-A3B-mlx-4Bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Open4bits/Qwen3.6-35B-A3B-mlx-4Bit with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Open4bits/Qwen3.6-35B-A3B-mlx-4Bit"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Open4bits/Qwen3.6-35B-A3B-mlx-4Bit
Run Hermes
hermes
- Docker Model Runner
How to use Open4bits/Qwen3.6-35B-A3B-mlx-4Bit with Docker Model Runner:
docker model run hf.co/Open4bits/Qwen3.6-35B-A3B-mlx-4Bit
| { | |
| "architectures": [ | |
| "Qwen3_5MoeForConditionalGeneration" | |
| ], | |
| "eos_token_id": [ | |
| 248046, | |
| 248044 | |
| ], | |
| "image_token_id": 248056, | |
| "model_type": "qwen3_5_moe", | |
| "quantization": { | |
| "group_size": 64, | |
| "bits": 4, | |
| "mode": "affine", | |
| "language_model.model.layers.0.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.0.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.1.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.1.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.2.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.2.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.3.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.3.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.4.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.4.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.5.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.5.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.6.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.6.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.7.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.7.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.8.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.8.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.9.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.9.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.10.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.10.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.11.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.11.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.12.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.12.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.13.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.13.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.14.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.14.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.15.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.15.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.16.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.16.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.17.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.17.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.18.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.18.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.19.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.19.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.20.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.20.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.21.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.21.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.22.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.22.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.23.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.23.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.24.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.24.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.25.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.25.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.26.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.26.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.27.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.27.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.28.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.28.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.29.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.29.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.30.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.30.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.31.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.31.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.32.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.32.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.33.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.33.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.34.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.34.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.35.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.35.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.36.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.36.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.37.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.37.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.38.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.38.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.39.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.39.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| } | |
| }, | |
| "quantization_config": { | |
| "group_size": 64, | |
| "bits": 4, | |
| "mode": "affine", | |
| "language_model.model.layers.0.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.0.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.1.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.1.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.2.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.2.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.3.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.3.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.4.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.4.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.5.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.5.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.6.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.6.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.7.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.7.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.8.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.8.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.9.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.9.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.10.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.10.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.11.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.11.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.12.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.12.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.13.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.13.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.14.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.14.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.15.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.15.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.16.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.16.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.17.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.17.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.18.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.18.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.19.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.19.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.20.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.20.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.21.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.21.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.22.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.22.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.23.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.23.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.24.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.24.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.25.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.25.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.26.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.26.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.27.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.27.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.28.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.28.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.29.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.29.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.30.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.30.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.31.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.31.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.32.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.32.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.33.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.33.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.34.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.34.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.35.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.35.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.36.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.36.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.37.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.37.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.38.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.38.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.39.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
| "language_model.model.layers.39.mlp.shared_expert_gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| } | |
| }, | |
| "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": 2048, | |
| "initializer_range": 0.02, | |
| "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_conv_kernel_dim": 4, | |
| "linear_key_head_dim": 128, | |
| "linear_num_key_heads": 16, | |
| "linear_num_value_heads": 32, | |
| "linear_value_head_dim": 128, | |
| "mamba_ssm_dtype": "float32", | |
| "max_position_embeddings": 262144, | |
| "model_type": "qwen3_5_moe_text", | |
| "moe_intermediate_size": 512, | |
| "mtp_num_hidden_layers": 1, | |
| "mtp_use_dedicated_embeddings": false, | |
| "num_attention_heads": 16, | |
| "num_experts": 256, | |
| "num_experts_per_tok": 8, | |
| "num_hidden_layers": 40, | |
| "num_key_value_heads": 2, | |
| "output_router_logits": false, | |
| "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, | |
| "type": "default" | |
| }, | |
| "router_aux_loss_coef": 0.001, | |
| "shared_expert_intermediate_size": 512, | |
| "tie_word_embeddings": false, | |
| "use_cache": true, | |
| "vocab_size": 248320 | |
| }, | |
| "tie_word_embeddings": false, | |
| "transformers_version": "4.57.1", | |
| "video_token_id": 248057, | |
| "vision_end_token_id": 248054, | |
| "vision_start_token_id": 248053 | |
| } |