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
```

MetaDiffusion-600M-ChatBase

File size: 321 Bytes

d59ecc4

{
  "temperature": 0.7,
  "repetition_penalty": 1.5,
  "num_steps": 128,
  "max_new_tokens": 96,
  "top_p": 0.0,
  "min_p": 0.1,
  "im_end_bias": 2.0,
  "im_end_bias_t": 0.3,
  "do_sample": true,
  "transformers_version": "4.49.0",
  "eos_token_id": [
    151645
  ],
  "pad_token_id": 151643,
  "mask_token_id": 151669
}