Text Generation
Transformers
Safetensors
mimo_gdn
mimo
linear-attention
gated-deltanet
hybrid
distillation
opd
long-context
conversational
Instructions to use arianraje/mimo-7b-gdn-hybrid-2.0B-OPD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arianraje/mimo-7b-gdn-hybrid-2.0B-OPD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="arianraje/mimo-7b-gdn-hybrid-2.0B-OPD") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("arianraje/mimo-7b-gdn-hybrid-2.0B-OPD", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use arianraje/mimo-7b-gdn-hybrid-2.0B-OPD with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "arianraje/mimo-7b-gdn-hybrid-2.0B-OPD" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "arianraje/mimo-7b-gdn-hybrid-2.0B-OPD", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/arianraje/mimo-7b-gdn-hybrid-2.0B-OPD
- SGLang
How to use arianraje/mimo-7b-gdn-hybrid-2.0B-OPD 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 "arianraje/mimo-7b-gdn-hybrid-2.0B-OPD" \ --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": "arianraje/mimo-7b-gdn-hybrid-2.0B-OPD", "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 "arianraje/mimo-7b-gdn-hybrid-2.0B-OPD" \ --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": "arianraje/mimo-7b-gdn-hybrid-2.0B-OPD", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use arianraje/mimo-7b-gdn-hybrid-2.0B-OPD with Docker Model Runner:
docker model run hf.co/arianraje/mimo-7b-gdn-hybrid-2.0B-OPD
| { | |
| "total_params": 8309898304, | |
| "inherited_params": 8301930496, | |
| "inherited_fraction": 0.9990411666053525, | |
| "dropped_teacher_bias_params": 165888, | |
| "teacher": "/ocean/projects/cis250011p/araje1/cache/hf_cache/hub/models--XiaomiMiMo--MiMo-7B-RL-0530/snapshots/323400599af3903adc2a536d6340a23fee88d2e0", | |
| "teacher_family": "mimo", | |
| "teacher_unexpected_keys_ignored": 16, | |
| "teacher_params": 7622619136, | |
| "full_attention_layers": [ | |
| 3, | |
| 7, | |
| 11, | |
| 15, | |
| 19, | |
| 23, | |
| 27, | |
| 31, | |
| 35 | |
| ], | |
| "surgery": { | |
| "full_attention_layers": null, | |
| "interval": 4, | |
| "linear_num_key_heads": 16, | |
| "linear_num_value_heads": 32, | |
| "linear_key_head_dim": 128, | |
| "linear_value_head_dim": 128, | |
| "linear_conv_kernel_dim": 4, | |
| "conv_init": "default" | |
| } | |
| } |