Text Generation
Transformers
Safetensors
inkling_mm_model
image-text-to-text
amd-quark
mxfp4
rocm
tokenspeed
8-bit precision
quark
Instructions to use lightseekorg/Inkling-MXFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lightseekorg/Inkling-MXFP4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lightseekorg/Inkling-MXFP4")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("lightseekorg/Inkling-MXFP4") model = AutoModelForMultimodalLM.from_pretrained("lightseekorg/Inkling-MXFP4") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use lightseekorg/Inkling-MXFP4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lightseekorg/Inkling-MXFP4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lightseekorg/Inkling-MXFP4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lightseekorg/Inkling-MXFP4
- SGLang
How to use lightseekorg/Inkling-MXFP4 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 "lightseekorg/Inkling-MXFP4" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lightseekorg/Inkling-MXFP4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "lightseekorg/Inkling-MXFP4" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lightseekorg/Inkling-MXFP4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lightseekorg/Inkling-MXFP4 with Docker Model Runner:
docker model run hf.co/lightseekorg/Inkling-MXFP4
Ctrl+K
- lora
- tiktoken
- 7.82 kB
- 3.18 kB
- 2.69 GB xet
- 41.8 kB
- 12.9 GB xet
- 3.45 GB xet
- 5.6 GB xet
- 5.36 GB xet
- 5.21 GB xet
- 5.44 GB xet
- 5.34 GB xet
- 5.13 GB xet
- 2.72 GB xet
- 5.39 GB xet
- 5.79 GB xet
- 5.13 GB xet
- 2.63 GB xet
- 5.13 GB xet
- 5.24 GB xet
- 5.46 GB xet
- 2.68 GB xet
- 5.5 GB xet
- 2.57 GB xet
- 5.45 GB xet
- 5.74 GB xet
- 3.12 GB xet
- 5.57 GB xet
- 5.27 GB xet
- 5.54 GB xet
- 5.8 GB xet
- 5.26 GB xet
- 5.59 GB xet
- 5.44 GB xet
- 5.15 GB xet
- 2.95 GB xet
- 5.66 GB xet
- 5.29 GB xet
- 5.32 GB xet
- 5.15 GB xet
- 5.75 GB xet
- 5.74 GB xet
- 5.21 GB xet
- 5.21 GB xet
- 5.54 GB xet
- 5.35 GB xet
- 5.72 GB xet
- 5.38 GB xet
- 5.9 GB xet