Text Generation
Transformers
Safetensors
English
expivme_diffusion
feature-extraction
language-model
transformer
rope
swiglu
diffusion
masked-diffusion
discrete-diffusion
from-scratch
tiny
small
experimental
custom_code
Instructions to use IvmeLabs/ExpIvme-DiffusionConversate-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IvmeLabs/ExpIvme-DiffusionConversate-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IvmeLabs/ExpIvme-DiffusionConversate-v1", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("IvmeLabs/ExpIvme-DiffusionConversate-v1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IvmeLabs/ExpIvme-DiffusionConversate-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IvmeLabs/ExpIvme-DiffusionConversate-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IvmeLabs/ExpIvme-DiffusionConversate-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/IvmeLabs/ExpIvme-DiffusionConversate-v1
- SGLang
How to use IvmeLabs/ExpIvme-DiffusionConversate-v1 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 "IvmeLabs/ExpIvme-DiffusionConversate-v1" \ --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": "IvmeLabs/ExpIvme-DiffusionConversate-v1", "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 "IvmeLabs/ExpIvme-DiffusionConversate-v1" \ --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": "IvmeLabs/ExpIvme-DiffusionConversate-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use IvmeLabs/ExpIvme-DiffusionConversate-v1 with Docker Model Runner:
docker model run hf.co/IvmeLabs/ExpIvme-DiffusionConversate-v1
- Xet hash:
- 6d256a7225ed14f95f8b83b611f393d5e19a2500b50bd31c92c95e4f9419882c
- Size of remote file:
- 520 MB
- SHA256:
- 8cf9a28d43e1ca52a0991d6d66a60c86927f737a3934ea650115a696ff5d97f0
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