Instructions to use IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct 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-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct

SGLang

How to use IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct 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-Instruct" \
    --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": "IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct",
		"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 "IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct" \
        --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": "IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct with Docker Model Runner:
```
docker model run hf.co/IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct
```

ExpIvme-DiffusionConversate-v1-Instruct / config.json

ereniko

SFT from IvmeLabs/ExpIvme-DiffusionConversate-v1 on HuggingFaceTB/smoltalk/everyday-conversations, 3 epochs

aea2066 verified about 8 hours ago

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	{
	"architectures": [
	"ExpIvmeForDiffusionLMHub"
	],
	"assistant_token_id": 16002,
	"context_len": 1024,
	"dropout": 0.0,
	"dtype": "float32",
	"endturn_token_id": 16003,
	"ffn_mult": 4.0,
	"hidden_dim": 896,
	"hidden_size": 896,
	"mask_token_id": 16000,
	"max_position_embeddings": 1024,
	"model_type": "expivme_diffusion",
	"n_heads": 14,
	"n_layers": 12,
	"norm_eps": 1e-05,
	"num_attention_heads": 14,
	"num_hidden_layers": 12,
	"rope_theta": 10000.0,
	"tie_word_embeddings": true,
	"transformers_version": "5.13.1",
	"user_token_id": 16001,
	"vocab_size": 16004,
	"auto_map": {
	"AutoConfig": "modeling_expivme_diffusion.ExpIvmeDiffusionConfig",
	"AutoModel": "modeling_expivme_diffusion.ExpIvmeForDiffusionLMHub"
	}
	}