Instructions to use CodeSoft/MetaDiffusion-600M-ChatBase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodeSoft/MetaDiffusion-600M-ChatBase with Transformers:

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

pipe = pipeline("text-generation", model="CodeSoft/MetaDiffusion-600M-ChatBase", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("CodeSoft/MetaDiffusion-600M-ChatBase", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CodeSoft/MetaDiffusion-600M-ChatBase with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CodeSoft/MetaDiffusion-600M-ChatBase"
# 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-600M-ChatBase",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CodeSoft/MetaDiffusion-600M-ChatBase

SGLang

How to use CodeSoft/MetaDiffusion-600M-ChatBase 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-600M-ChatBase" \
    --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-600M-ChatBase",
		"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-600M-ChatBase" \
        --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-600M-ChatBase",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CodeSoft/MetaDiffusion-600M-ChatBase with Docker Model Runner:
```
docker model run hf.co/CodeSoft/MetaDiffusion-600M-ChatBase
```

CodeSoft commited on 2 days ago

Commit

720a8a8

verified ·

1 Parent(s): 18eebaf

Add hfviewer architecture card

Browse files

Embed the hfviewer architecture visualization card in README.md.

Files changed (1) hide show

README.md +11 -1

README.md CHANGED Viewed

@@ -36,6 +36,16 @@ The AR checkpoint becomes the initialization (weights copied, timestep modules z
 - Vocab / IO: 151,677 rows = Qwen3's 151,669 + [MASK] (id 151669) + 7 rainbow padding tokens (151670-151676); pad_token_id is <|endoftext|> (151643), eos is <|im_end|> (151645). bf16 weights, 371 tensors in model.safetensors.
 ## Use with Transformers
 ```python
@@ -116,4 +126,4 @@ This model is an experimental research checkpoint intended for further fine-tuni
 ## License
-Apache-2.0

 - Vocab / IO: 151,677 rows = Qwen3's 151,669 + [MASK] (id 151669) + 7 rainbow padding tokens (151670-151676); pad_token_id is <|endoftext|> (151643), eos is <|im_end|> (151645). bf16 weights, 371 tensors in model.safetensors.
+## Architecture graph
+<a href="https://hfviewer.com/CodeSoft/MetaDiffusion-600M-ChatBase?utm_source=huggingface&amp;utm_medium=embedded_model_card&amp;utm_campaign=CodeSoft_MetaDiffusion-600M-ChatBase_card" target="_blank" rel="noopener">
+  <img
+    src="https://hfviewer.com/api/card.svg?source=CodeSoft%2FMetaDiffusion-600M-ChatBase&amp;granularity=0"
+    alt="Architecture graph for CodeSoft/MetaDiffusion-600M-ChatBase. Open in hfviewer"
+    width="100%"
+  />
+</a>
 ## Use with Transformers
 ```python
 ## License
+Apache-2.0