Text Generation
Transformers
Safetensors
mimo_v2
vllm
modelopt
nvfp4
quantized
Mixture of Experts
mimo
mimo-v2.6
agentic
tool-calling
blackwell
conversational
custom_code
8-bit precision
Instructions to use primitive-ai/MiMo-V2.6-Flash-RL-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use primitive-ai/MiMo-V2.6-Flash-RL-NVFP4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="primitive-ai/MiMo-V2.6-Flash-RL-NVFP4", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("primitive-ai/MiMo-V2.6-Flash-RL-NVFP4", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use primitive-ai/MiMo-V2.6-Flash-RL-NVFP4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "primitive-ai/MiMo-V2.6-Flash-RL-NVFP4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "primitive-ai/MiMo-V2.6-Flash-RL-NVFP4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/primitive-ai/MiMo-V2.6-Flash-RL-NVFP4
- SGLang
How to use primitive-ai/MiMo-V2.6-Flash-RL-NVFP4 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 "primitive-ai/MiMo-V2.6-Flash-RL-NVFP4" \ --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": "primitive-ai/MiMo-V2.6-Flash-RL-NVFP4", "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 "primitive-ai/MiMo-V2.6-Flash-RL-NVFP4" \ --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": "primitive-ai/MiMo-V2.6-Flash-RL-NVFP4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use primitive-ai/MiMo-V2.6-Flash-RL-NVFP4 with Docker Model Runner:
docker model run hf.co/primitive-ai/MiMo-V2.6-Flash-RL-NVFP4
Download model_pp0_ep28_shard0.safetensors from primitive-ai/MiMo-V2.6-Flash-RL-NVFP4: direct link, hf CLI and curl.
- Browser
- Download file 2.66 GB
-
https://huggingface.co/primitive-ai/MiMo-V2.6-Flash-RL-NVFP4/resolve/main/model_pp0_ep28_shard0.safetensors
- Command line
-
hf download hf://primitive-ai/MiMo-V2.6-Flash-RL-NVFP4/model_pp0_ep28_shard0.safetensors
-
curl -L -o model_pp0_ep28_shard0.safetensors https://huggingface.co/primitive-ai/MiMo-V2.6-Flash-RL-NVFP4/resolve/main/model_pp0_ep28_shard0.safetensors
2.66 GB
- Xet hash:
- 41736bdaf7c8163caeb505232d2a4750429ef16c4ec9439c40f9bca9d511991c
- Size of remote file:
- 2.66 GB
- SHA256:
- 3f9dc0618b425346fc7aac4390c7026343ffd03dc50631935552eef739f99a1f
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