Instructions to use CodeSoft/MetaDiffusion-150M-exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodeSoft/MetaDiffusion-150M-exp with Transformers:

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

pipe = pipeline("text-generation", model="CodeSoft/MetaDiffusion-150M-exp")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("CodeSoft/MetaDiffusion-150M-exp", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CodeSoft/MetaDiffusion-150M-exp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CodeSoft/MetaDiffusion-150M-exp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodeSoft/MetaDiffusion-150M-exp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CodeSoft/MetaDiffusion-150M-exp

SGLang

How to use CodeSoft/MetaDiffusion-150M-exp 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 "CodeSoft/MetaDiffusion-150M-exp" \
    --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": "CodeSoft/MetaDiffusion-150M-exp",
		"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 "CodeSoft/MetaDiffusion-150M-exp" \
        --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": "CodeSoft/MetaDiffusion-150M-exp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CodeSoft/MetaDiffusion-150M-exp with Docker Model Runner:
```
docker model run hf.co/CodeSoft/MetaDiffusion-150M-exp
```

CodeSoft commited on 3 days ago

Commit

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@@ -19,11 +19,24 @@ base_model:
 - SupraLabs/Supra-1.5-50M-Base-exp
 ---
-<h1 align="center">MetaDiffusion-150M-exp</h1>
-<p align="center">
-  This model is an experimental release. Do not use it in production!
-</p>
 ## Architecture

 - SupraLabs/Supra-1.5-50M-Base-exp
 ---
+<div align="center">
+  <h1>MetaDiffusion-150M-exp</h1>
+  <a href="https://hfviewer.com/CodeSoft/MetaDiffusion-150M-exp?utm_source=huggingface&amp;utm_medium=embedded_model_card&amp;utm_campaign=CodeSoft__MetaDiffusion-150M-exp_card&amp;utm_content=embedded_card_open_viewer&amp;from=embedded-model-card" target="_blank" rel="noopener" style="display: inline-block; max-width: 720px; width: 100%;">
+    <img
+      src="https://hfviewer.com/api/model-og-card.png?source=CodeSoft%2FMetaDiffusion-150M-exp&amp;embed_style=summary-card&amp;v=20260629-og-direct-preview-v1"
+      alt="Summary architecture graph card for CodeSoft/MetaDiffusion-150M-exp. Open in hfviewer"
+      width="100%"
+      style="width: 100%; height: auto; border-radius: 12px; border: 1px solid #e1e4e8; box-shadow: 0 4px 12px rgba(0,0,0,0.08);"
+    />
+  </a>
+  <p>
+    This model is for research and evaluation purposes only. Do not use in production environments.
+  </p>
+</div>
 ## Architecture